Step4.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNQ+5nNDtgN3sOK/tcjVeRy"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"premium"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Mg1RO7cMgyG_","executionInfo":{"status":"ok","timestamp":1670685437061,"user_tz":-480,"elapsed":1156,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"b4d5f3c5-82f3-4150-a05e-c824a103d5e9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Sat Dec 10 15:17:17 2022       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  A100-SXM4-40GB      Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   34C    P0    51W / 400W |      0MiB / 40536MiB |      0%      Default |\n","|                               |                      |             Disabled |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/gdrive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VgvxuSShg2Tc","executionInfo":{"status":"ok","timestamp":1670685452760,"user_tz":-480,"elapsed":15701,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"7e6cb3f7-60a8-44fa-c0a5-2af282b18192"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /gdrive\n"]}]},{"cell_type":"code","source":["# Download YOLOv7 repository and install requirements\n","\n","%cd /content\n","!git clone https://github.com/WongKinYiu/yolov7.git\n","%cd yolov7\n","!pip install -r requirements.txt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5K4jyNDbg3WK","executionInfo":{"status":"ok","timestamp":1670685460497,"user_tz":-480,"elapsed":7741,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"a457cbec-9e94-4487-b0cb-b86def9c0317"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n","Cloning into 'yolov7'...\n","remote: Enumerating objects: 1094, done.\u001b[K\n","remote: Counting objects: 100% (3/3), done.\u001b[K\n","remote: Compressing objects: 100% (3/3), done.\u001b[K\n","remote: Total 1094 (delta 0), reused 1 (delta 0), pack-reused 1091\u001b[K\n","Receiving objects: 100% (1094/1094), 69.85 MiB | 46.03 MiB/s, done.\n","Resolving deltas: 100% (522/522), done.\n","/content/yolov7\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 4)) (3.2.2)\n","Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 5)) (1.21.6)\n","Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 6)) (4.6.0.66)\n","Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 7)) (7.1.2)\n","Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 8)) (6.0)\n","Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 9)) (2.23.0)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 10)) (1.7.3)\n","Requirement already satisfied: torch!=1.12.0,>=1.7.0 in 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in /usr/local/lib/python3.8/dist-packages (from ipython->-r requirements.txt (line 34)) (2.0.10)\n","Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython->-r requirements.txt (line 34)) (2.6.1)\n","Collecting jedi>=0.10\n","  Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n","\u001b[K     |████████████████████████████████| 1.6 MB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython->-r requirements.txt (line 34)) (4.4.2)\n","Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->ipython->-r requirements.txt (line 34)) (0.8.3)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->-r requirements.txt (line 34)) (0.2.5)\n","Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->ipython->-r requirements.txt (line 34)) (0.7.0)\n","Installing collected packages: jedi, thop\n","Successfully installed jedi-0.18.2 thop-0.1.1.post2209072238\n"]}]},{"cell_type":"code","source":["!sed -n -e 1557p -e 1404p /content/yolov7/utils/loss.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hRCGOmUET5B_","executionInfo":{"status":"ok","timestamp":1670685460497,"user_tz":-480,"elapsed":7,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"b0e13069-92ed-4367-bfc8-69da28ff676a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["            from_which_layer = from_which_layer[fg_mask_inboxes]\n","            from_which_layer = from_which_layer[fg_mask_inboxes]\n"]}]},{"cell_type":"markdown","source":["第1557和第1404要修改 不然會Error"],"metadata":{"id":"Bqy4yV8_UTRK"}},{"cell_type":"code","source":["# 注意跳脫自字元\n","!sed -i '1557s/from_which_layer\\[fg_mask_inboxes\\]/from_which_layer.to(fg_mask_inboxes.device)\\[fg_mask_inboxes\\]/' /content/yolov7/utils/loss.py\n","!sed -i '1404s/from_which_layer\\[fg_mask_inboxes\\]/from_which_layer.to(fg_mask_inboxes.device)\\[fg_mask_inboxes\\]/' /content/yolov7/utils/loss.py\n","\n","!sed -n -e 1557p -e 1404p /content/yolov7/utils/loss.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4c-Ye2q_WTHa","executionInfo":{"status":"ok","timestamp":1670685460925,"user_tz":-480,"elapsed":432,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"ce59d3ad-b97e-4c9d-d176-b8280dce8d30"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["            from_which_layer = from_which_layer.to(fg_mask_inboxes.device)[fg_mask_inboxes]\n","            from_which_layer = from_which_layer.to(fg_mask_inboxes.device)[fg_mask_inboxes]\n"]}]},{"cell_type":"code","source":["import os\n","%cd /content/yolov7\n","\n","'''\n","!gdown --id '1y4L4uB97FgVdFZyOw8NiGkVKKlnQHYsx'\n","!unzip -q mydataset1000.zip\n","os.remove(\"mydataset1000.zip\")\n","'''\n","\n","!cp '/gdrive/MyDrive/Alex_Lin/無人機比賽/mydataset1000.zip' '/content/yolov7/mydataset1000.zip'\n","!unzip -q 'mydataset1000.zip'\n","os.remove(\"mydataset1000.zip\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EfRYq_iug4Zs","executionInfo":{"status":"ok","timestamp":1670685622219,"user_tz":-480,"elapsed":161296,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"db2e3383-fd90-45ee-9844-7c1505fb9298"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/yolov7\n"]}]},{"cell_type":"code","source":["yaml_file = \"/content/yolov7/mydataset1000/data.yaml\"\n","if os.path.exists(yaml_file) == False:\n","  f= open(yaml_file,\"w+\")\n","  f.write(\"train: /content/yolov7/mydataset1000/train/images\\n\") # train資料位置\n","  f.write(\"val: /content/yolov7/mydataset1000/val/images\\n\")\n","  f.write(\"nc: 4\\n\") # 修改:有幾個類別\n","  f.write(\"\\n\")\n","  # 自己dataset的類別\n","  f.write(\"names: ['car', 'hov', 'person', 'motorcycle']\\n\") # 修改\n","  f.close()\n"],"metadata":{"id":"bvNjYkVZg7Kg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"rIXtGebhnQCy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!sed -n -e 8p -e 9p -e 10p -e 11p cfg/training/yolov7-w6.yaml"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AwsCSs4snQU5","executionInfo":{"status":"ok","timestamp":1670685622220,"user_tz":-480,"elapsed":8,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"4c1de6f2-4ee8-41cc-e187-850d689d9838"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["  - [ 19,27,  44,40,  38,94 ]  # P3/8\n","  - [ 96,68,  86,152,  180,137 ]  # P4/16\n","  - [ 140,301,  303,264,  238,542 ]  # P5/32\n","  - [ 436,615,  739,380,  925,792 ]  # P6/64\n"]}]},{"cell_type":"code","source":["!sed -i '8s/19,27,  44,40,  38,94/6,21,  13,14,  11,23/' cfg/training/yolov7-w6.yaml\n","!sed -i '9s/96,68,  86,152,  180,137/18,23,  13,34,  30,26/' cfg/training/yolov7-w6.yaml\n","!sed -i '10s/140,301,  303,264,  238,542/22,37,  17,53,  22,71/' cfg/training/yolov7-w6.yaml\n","!sed -i '11s/436,615,  739,380,  925,792/40,39,  37,67,  77,91/' cfg/training/yolov7-w6.yaml\n","\n","!sed -n -e 8p -e 9p -e 10p -e 11p cfg/training/yolov7-w6.yaml"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H5SOAHegnSOD","executionInfo":{"status":"ok","timestamp":1670685622826,"user_tz":-480,"elapsed":610,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"8555a422-effe-45d3-e4b6-1cfcc79c6e23"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["  - [ 6,21,  13,14,  11,23 ]  # P3/8\n","  - [ 18,23,  13,34,  30,26 ]  # P4/16\n","  - [ 22,37,  17,53,  22,71 ]  # P5/32\n","  - [ 40,39,  37,67,  77,91 ]  # P6/64\n"]}]},{"cell_type":"code","source":["%cd /content/yolov7\n","!pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O9PnBEJ9g8RN","executionInfo":{"status":"ok","timestamp":1670685623275,"user_tz":-480,"elapsed":451,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"5a9b9262-6e38-4c12-b9af-a90e17ccf321"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/yolov7\n","/content/yolov7\n"]}]},{"cell_type":"markdown","source":["下載前一階段在VisDrone訓練4類別的權重檔準備接類訓練"],"metadata":{"id":"ePGor7WPhAOk"}},{"cell_type":"code","source":["''' gdown 常常很奇怪\n","!gdown --id '1sFRR7dgHlrt2ZMvF4Udecz2D4hTM95l2' --output data.zip\n","!unzip -q data.zip\n","os.remove(\"data.zip\")\n","'''\n","\n","!cp '/gdrive/MyDrive/Alex_Lin/無人機比賽/用到VisDrone/3_best.zip' '/content/yolov7/3_best.zip'\n","!unzip -q '3_best.zip'\n","os.remove(\"3_best.zip\")"],"metadata":{"id":"JUwOQXdFg9X6"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 使用 wandb 記錄訓練過程\n","- 先官網登入：https://wandb.ai/site \n","- 右上方頭像 -> settings -> 複製 API keys -> 填入下方輸入處"],"metadata":{"id":"ro2A96kHhCaH"}},{"cell_type":"code","source":["!pip install wandb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QbTrqfLxhC2x","executionInfo":{"status":"ok","timestamp":1670685638233,"user_tz":-480,"elapsed":9344,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"fdd37b0d-5abc-4ddd-b410-65542cfd8d13"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting wandb\n","  Downloading wandb-0.13.6-py2.py3-none-any.whl (1.9 MB)\n","\u001b[K     |████████████████████████████████| 1.9 MB 5.1 MB/s \n","\u001b[?25hRequirement already 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MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.10-py2.py3-none-any.whl (162 kB)\n","\u001b[K     |████████████████████████████████| 162 kB 79.9 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.9-py2.py3-none-any.whl (162 kB)\n","\u001b[K     |████████████████████████████████| 162 kB 72.5 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.8-py2.py3-none-any.whl (158 kB)\n","\u001b[K     |████████████████████████████████| 158 kB 93.3 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.7-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 101.0 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.6-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 99.8 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.5-py2.py3-none-any.whl (157 kB)\n","\u001b[K     |████████████████████████████████| 157 kB 78.5 MB/s \n","\u001b[?25h  Downloading sentry_sdk-1.9.4-py2.py3-none-any.whl (157 kB)\n","\u001b[K     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/root/.cache/pip/wheels/4c/8e/7e/72fbc243e1aeecae64a96875432e70d4e92f3d2d18123be004\n","Successfully built pathtools\n","Installing collected packages: smmap, gitdb, shortuuid, setproctitle, sentry-sdk, pathtools, GitPython, docker-pycreds, wandb\n","Successfully installed GitPython-3.1.29 docker-pycreds-0.4.0 gitdb-4.0.10 pathtools-0.1.2 sentry-sdk-1.9.0 setproctitle-1.3.2 shortuuid-1.0.11 smmap-5.0.0 wandb-0.13.6\n"]}]},{"cell_type":"code","source":["import wandb\n","# Logging\n","id = wandb.util.generate_id()\n","wandb_run = wandb.init()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"b0RWM23HhFuU","executionInfo":{"status":"ok","timestamp":1670685653013,"user_tz":-480,"elapsed":14799,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"8ca8974f-aa94-45c8-d00e-674aa4d0aa9a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["ERROR:wandb.jupyter:Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","        window._wandbApiKey = new Promise((resolve, reject) => {\n","            function loadScript(url) {\n","            return new Promise(function(resolve, reject) {\n","                let newScript = document.createElement(\"script\");\n","                newScript.onerror = reject;\n","                newScript.onload = resolve;\n","                document.body.appendChild(newScript);\n","                newScript.src = url;\n","            });\n","            }\n","            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n","            const iframe = document.createElement('iframe')\n","            iframe.style.cssText = \"width:0;height:0;border:none\"\n","            document.body.appendChild(iframe)\n","            const handshake = new Postmate({\n","                container: iframe,\n","                url: 'https://wandb.ai/authorize'\n","            });\n","            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n","            handshake.then(function(child) {\n","                child.on('authorize', data => {\n","                    clearTimeout(timeout)\n","                    resolve(data)\n","                });\n","            });\n","            })\n","        });\n","    "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.13.6"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/yolov7/wandb/run-20221210_152047-2jpov23h</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href=\"https://wandb.ai/tunyu/yolov7/runs/2jpov23h\" target=\"_blank\">rich-dream-9</a></strong> to <a href=\"https://wandb.ai/tunyu/yolov7\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"]},"metadata":{}}]},{"cell_type":"code","source":["'''如果train.py那邊有error 又重新執行的話，最好在跑一次id\n","我本來看 id是 29mx03y8\n","但執行train.py時batch-size 8不能執行\n","我改成 batch-size 4 之後id好像有改\n","在wandb上看到變成 29mx03y82\n","'''\n","id \n","# 把亂碼印出來 到wandb看結果時就找這組亂碼 是對到這次訓練"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":38},"id":"IVtc8GJBhFz-","executionInfo":{"status":"ok","timestamp":1670685653013,"user_tz":-480,"elapsed":13,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"f02d0e0d-bac2-4b14-8bac-05a98fea0655"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'j33oolsz'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","source":["### 開始訓練"],"metadata":{"id":"M1HSrNIGhIW_"}},{"cell_type":"code","source":["# 注意--data和--weights的路徑\n","\n","!python train_aux.py --epochs 500 \\\n","--batch-size 22 \\\n","--data mydataset1000/data.yaml \\\n","--img 1280 1280 \\\n","--device 0 \\\n","--cfg cfg/training/yolov7-w6.yaml \\\n","--weights '3_best.pt' \\\n","--hyp data/hyp.scratch.custom.yaml"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nRZxNXA8hQbP","executionInfo":{"status":"ok","timestamp":1670724818630,"user_tz":-480,"elapsed":30392937,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"2d65f304-2f54-484f-8bae-a6dc0b44d742"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["YOLOR 🚀 v0.1-116-g8c0bf3f torch 1.13.0+cu116 CUDA:0 (A100-SXM4-40GB, 40536.1875MB)\n","\n","Namespace(adam=False, artifact_alias='latest', batch_size=22, bbox_interval=-1, bucket='', cache_images=False, cfg='cfg/training/yolov7-w6.yaml', data='mydataset1000/data.yaml', device='0', entity=None, epochs=500, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.custom.yaml', image_weights=False, img_size=[1280, 1280], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=22, upload_dataset=False, v5_metric=False, weights='3_best.pt', workers=8, world_size=1)\n","\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n","2022-12-10 15:20:59.368368: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n","\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, paste_in=0.0, loss_ota=1\n","\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mtunyu\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.13.6\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/yolov7/wandb/run-20221210_152101-578by4qv\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mexp\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/tunyu/YOLOR\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/tunyu/YOLOR/runs/578by4qv\u001b[0m\n","Overriding model.yaml nc=80 with nc=4\n","\n","                 from  n    params  module                                  arguments                     \n","  0                -1  1         0  models.common.ReOrg                     []                            \n","  1                -1  1     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33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 11                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n"," 12                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 13                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 14                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 16                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 17                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 18  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 19                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 20                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n"," 21                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 22                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 23                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 24                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 25                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 26                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 27  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 28                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n"," 29                -1  1   3540480  models.common.Conv                      [512, 768, 3, 2]              \n"," 30                -1  1    295680  models.common.Conv                      [768, 384, 1, 1]              \n"," 31                -2  1    295680  models.common.Conv                      [768, 384, 1, 1]              \n"," 32                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]              \n"," 33                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]              \n"," 34                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]              \n"," 35                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]              \n"," 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 37                -1  1   1181184  models.common.Conv                      [1536, 768, 1, 1]             \n"," 38                -1  1   7079936  models.common.Conv                      [768, 1024, 3, 2]             \n"," 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n"," 40                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n"," 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n"," 42                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n"," 43                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n"," 44                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n"," 45  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 46                -1  1   2099200  models.common.Conv                      [2048, 1024, 1, 1]            \n"," 47                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                \n"," 48                -1  1    197376  models.common.Conv                      [512, 384, 1, 1]              \n"," 49                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n"," 50                37  1    295680  models.common.Conv                      [768, 384, 1, 1]              \n"," 51          [-1, -2]  1         0  models.common.Concat                    [1]                           \n"," 52                -1  1    295680  models.common.Conv                      [768, 384, 1, 1]              \n"," 53                -2  1    295680  models.common.Conv                      [768, 384, 1, 1]              \n"," 54                -1  1    663936  models.common.Conv                      [384, 192, 3, 1]              \n"," 55                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]              \n"," 56                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]              \n"," 57                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]              \n"," 58[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 59                -1  1    590592  models.common.Conv                      [1536, 384, 1, 1]             \n"," 60                -1  1     98816  models.common.Conv                      [384, 256, 1, 1]              \n"," 61                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n"," 62                28  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 63          [-1, -2]  1         0  models.common.Concat                    [1]                           \n"," 64                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 65                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 66                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n"," 67                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 68                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 69                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 70[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 71                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n"," 72                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 73                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n"," 74                19  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 75          [-1, -2]  1         0  models.common.Concat                    [1]                           \n"," 76                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 77                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 78                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               \n"," 79                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n"," 80                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n"," 81                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n"," 82[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 83                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n"," 84                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n"," 85          [-1, 71]  1         0  models.common.Concat                    [1]                           \n"," 86                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 87                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 88                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n"," 89                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 90                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 91                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 92[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 93                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n"," 94                -1  1    885504  models.common.Conv                      [256, 384, 3, 2]              \n"," 95          [-1, 59]  1         0  models.common.Concat                    [1]                           \n"," 96                -1  1    295680  models.common.Conv                      [768, 384, 1, 1]              \n"," 97                -2  1    295680  models.common.Conv                      [768, 384, 1, 1]              \n"," 98                -1  1    663936  models.common.Conv                      [384, 192, 3, 1]              \n"," 99                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]              \n","100                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]              \n","101                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]              \n","102[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n","103                -1  1    590592  models.common.Conv                      [1536, 384, 1, 1]             \n","104                -1  1   1770496  models.common.Conv                      [384, 512, 3, 2]              \n","105          [-1, 47]  1         0  models.common.Concat                    [1]                           \n","106                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n","107                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n","108                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              \n","109                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n","110                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n","111                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n","112[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n","113                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             \n","114                83  1    295424  models.common.Conv                      [128, 256, 3, 1]              \n","115                93  1   1180672  models.common.Conv                      [256, 512, 3, 1]              \n","116               103  1   2655744  models.common.Conv                      [384, 768, 3, 1]              \n","117               113  1   4720640  models.common.Conv                      [512, 1024, 3, 1]             \n","118                83  1    369280  models.common.Conv                      [128, 320, 3, 1]              \n","119                71  1   1475840  models.common.Conv                      [256, 640, 3, 1]              \n","120                59  1   3319680  models.common.Conv                      [384, 960, 3, 1]              \n","121                47  1   5900800  models.common.Conv                      [512, 1280, 3, 1]             \n","122[114, 115, 116, 117, 118, 119, 120, 121]  1    158404  models.yolo.IAuxDetect                  [4, [[6, 21, 13, 14, 11, 23], [18, 23, 13, 34, 30, 26], [22, 37, 17, 53, 22, 71], [40, 39, 37, 67, 77, 91]], [256, 512, 768, 1024, 320, 640, 960, 1280]]\n","/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n","  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n","Model Summary: 477 layers, 80996420 parameters, 80996420 gradients, 102.5 GFLOPS\n","\n","Transferred 666/668 items from 3_best.pt\n","Scaled weight_decay = 0.000515625\n","Optimizer groups: 115 .bias, 115 conv.weight, 115 other\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov7/mydataset1000/train/labels' images and labels... 904 found, 0 missing, 0 empty, 0 corrupted:  90% 904/1000 [00:06<00:00, 153.14it/s]\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/mydataset1000/train/images/img0914.png: duplicate labels\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov7/mydataset1000/train/labels' images and labels... 1000 found, 0 missing, 0 empty, 1 corrupted: 100% 1000/1000 [00:06<00:00, 150.34it/s]\n","\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov7/mydataset1000/train/labels.cache\n","\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/yolov7/mydataset1000/val/labels' images and labels... 908 found, 0 missing, 0 empty, 0 corrupted:  91% 908/1000 [00:08<00:00, 112.57it/s]\u001b[34m\u001b[1mval: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/mydataset1000/val/images/img0914.png: duplicate labels\n","\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/yolov7/mydataset1000/val/labels' images and labels... 1000 found, 0 missing, 0 empty, 1 corrupted: 100% 1000/1000 [00:09<00:00, 110.80it/s]\n","\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/yolov7/mydataset1000/val/labels.cache\n","\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 9.05, Best Possible Recall (BPR) = 0.9998\n","Image sizes 1280 train, 1280 test\n","Using 8 dataloader workers\n","Logging results to runs/train/exp\n","Starting training for 500 epochs...\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     0/499     38.7G   0.08337    0.1018  0.003055    0.1882       574      1280: 100% 46/46 [01:38<00:00,  2.14s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:21<00:00,  1.09it/s]\n","                 all         999       45009       0.746       0.657       0.703       0.299\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     1/499     38.7G   0.05697   0.08628  0.002378    0.1456       638      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.832       0.767       0.818       0.403\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     2/499     38.7G   0.04987   0.08623  0.002066    0.1382       846      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009        0.86       0.831       0.862       0.444\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     3/499     38.7G   0.04725   0.08541  0.001676    0.1343       928      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.868       0.857       0.881       0.462\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     4/499     38.7G   0.04567   0.08419  0.001395    0.1313       806      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.883       0.866       0.895       0.477\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     5/499     38.7G   0.04463   0.08197  0.001221    0.1278       587      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.891        0.87       0.903       0.485\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     6/499     38.7G   0.04367   0.07894  0.001127    0.1237       637      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.886       0.885       0.908       0.489\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     7/499     38.7G   0.04325   0.08258  0.001094    0.1269       747      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.897       0.888       0.916       0.498\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     8/499     38.7G   0.04234   0.07535  0.001057    0.1187       504      1280: 100% 46/46 [00:58<00:00,  1.28s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.904       0.895       0.922       0.505\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     9/499     38.7G   0.04245   0.07919 0.0009761    0.1226       835      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009         0.9       0.905       0.926       0.511\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    10/499     38.7G   0.04169   0.07964 0.0009156    0.1223       742      1280: 100% 46/46 [00:58<00:00,  1.28s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.911       0.903       0.929       0.509\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    11/499     38.7G   0.04222   0.08309 0.0008611    0.1262      1041      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.907       0.909        0.93       0.514\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    12/499     38.7G   0.04249   0.08397 0.0008276    0.1273       735      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009        0.91       0.905        0.93       0.518\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    13/499     38.7G   0.04192   0.08322 0.0008263     0.126       568      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.921       0.905       0.934       0.521\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    14/499     38.7G   0.04143   0.07899 0.0008242    0.1212       865      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.918       0.912       0.937       0.519\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    15/499     38.7G   0.04113   0.07942 0.0008605    0.1214       860      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009        0.92       0.913       0.938       0.524\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    16/499     38.7G   0.04116    0.0829 0.0008246    0.1249       539      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.917       0.912       0.936       0.515\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    17/499     38.7G   0.04446   0.08169  0.000874     0.127       651      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.912       0.915       0.935       0.513\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    18/499     38.7G   0.04326   0.08667 0.0009107    0.1308       761      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.48it/s]\n","                 all         999       45009       0.914       0.918       0.934       0.509\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    19/499     38.7G   0.04263   0.08051 0.0009078     0.124       581      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.923       0.915       0.938       0.521\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    20/499     38.7G   0.04318   0.08143 0.0009295    0.1255       853      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.928        0.91        0.94        0.52\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    21/499     38.7G   0.04269   0.08087 0.0008741    0.1244       690      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.929       0.915       0.941       0.528\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    22/499     38.7G   0.04234   0.08194 0.0008464    0.1251       885      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.929       0.924       0.947       0.523\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    23/499     38.7G   0.04124   0.08424 0.0007902    0.1263       699      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.927       0.923       0.944       0.528\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    24/499     38.7G   0.04125   0.08312 0.0008498    0.1252       676      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.927       0.926       0.948       0.536\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    25/499     38.7G   0.04203   0.08338 0.0009339    0.1263       963      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.926       0.924       0.945       0.526\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    26/499     38.7G   0.04099     0.083 0.0008171    0.1248       856      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.932        0.92       0.945       0.534\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    27/499     38.7G   0.04082   0.07981 0.0007591    0.1214       701      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.938       0.929       0.951       0.544\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    28/499     38.7G   0.04068    0.0831 0.0008104    0.1246       708      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.927        0.93       0.949       0.539\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    29/499     38.7G   0.04102   0.08337 0.0007823    0.1252       667      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.934        0.93       0.951        0.54\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    30/499     38.7G    0.0399   0.08192 0.0007933    0.1226       524      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009        0.93       0.926        0.95       0.542\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    31/499     38.7G    0.0397   0.07977 0.0006937    0.1202       490      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.935       0.931       0.952       0.548\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    32/499     38.7G    0.0404    0.0837 0.0007416    0.1248      1164      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.938       0.935       0.956        0.55\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    33/499     38.7G   0.03998   0.07998 0.0007086    0.1207       928      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.941       0.929       0.956       0.556\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    34/499     38.7G    0.0398   0.08134 0.0007363    0.1219       709      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009        0.94       0.931       0.955       0.547\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    35/499     38.7G    0.0402   0.08315 0.0007057    0.1241       681      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.942       0.928       0.955       0.545\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    36/499     38.7G   0.04072   0.08062 0.0007084    0.1221       491      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.94       0.931       0.955       0.547\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    37/499     38.7G   0.03975   0.08175 0.0006996    0.1222       644      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.53it/s]\n","                 all         999       45009        0.94       0.934       0.957       0.558\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    38/499     38.7G    0.0397   0.08041 0.0006699    0.1208       564      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.944       0.937        0.96       0.562\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    39/499     38.7G   0.03938   0.08017 0.0006412    0.1202       809      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.952       0.928       0.958       0.558\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    40/499     38.7G   0.03984   0.07926 0.0006864    0.1198       683      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.941       0.936       0.958       0.561\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    41/499     38.7G   0.03951   0.07998 0.0006357    0.1201       493      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.943       0.933       0.956       0.556\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    42/499     38.7G   0.03911    0.0788 0.0006509    0.1186       661      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009        0.94       0.936       0.958        0.56\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    43/499     38.7G   0.03908   0.07835 0.0006253    0.1181       857      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.946       0.936       0.959       0.559\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    44/499     38.7G   0.03886   0.07863 0.0006357    0.1181       555      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.947       0.939       0.962       0.567\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    45/499     38.7G   0.03911   0.08181 0.0005733    0.1215       617      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.943       0.932       0.958       0.556\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    46/499     38.7G   0.03898    0.0808 0.0005969    0.1204       663      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.942       0.939       0.961       0.565\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    47/499     38.7G   0.03899   0.07835 0.0006032    0.1179       834      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.944        0.94        0.96       0.565\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    48/499     38.7G   0.03879   0.08028  0.000624    0.1197       556      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.948       0.939       0.962       0.571\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    49/499     38.7G   0.03902    0.0807 0.0006337    0.1203       798      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.12it/s]\n","                 all         999       45009       0.951       0.942       0.964       0.571\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    50/499     38.7G     0.038   0.07641 0.0006184     0.115       578      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.952       0.942       0.964       0.574\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    51/499     38.7G   0.03854   0.08109 0.0005808    0.1202       909      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.947       0.944       0.964       0.573\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    52/499     38.7G   0.03874   0.08132 0.0005676    0.1206       672      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.944       0.939        0.96       0.566\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    53/499     38.7G   0.03842   0.07872 0.0005734    0.1177       636      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.938       0.944       0.961       0.563\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    54/499     38.7G   0.03904   0.08333 0.0006285     0.123       938      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.953       0.936       0.964       0.572\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    55/499     38.7G   0.03849   0.08237 0.0005745    0.1214       620      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.947        0.94       0.962       0.565\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    56/499     38.7G   0.03832   0.07904 0.0005963     0.118       868      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.946       0.942       0.962       0.565\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    57/499     38.7G   0.03918   0.07968 0.0005442    0.1194       635      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.949       0.939       0.961       0.565\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    58/499     38.7G   0.03901   0.07913 0.0005716    0.1187       840      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.945       0.939       0.961       0.571\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    59/499     38.7G   0.03844   0.08268 0.0006048    0.1217       991      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.95       0.942       0.965       0.576\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    60/499     38.7G   0.03867   0.07814  0.000613    0.1174       646      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.951       0.942       0.964       0.577\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    61/499     38.7G   0.03933   0.08427 0.0005467    0.1241      1045      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.949       0.947       0.967        0.58\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    62/499     38.7G   0.03856   0.07998 0.0006243    0.1192       617      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.949       0.949       0.968       0.579\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    63/499     38.7G   0.03807   0.07767 0.0005692    0.1163       625      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.948       0.943       0.965       0.576\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    64/499     38.7G    0.0394   0.08064 0.0005322    0.1206       475      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.955       0.942       0.966       0.574\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    65/499     38.7G   0.03813   0.07982 0.0005284    0.1185       797      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.956       0.945       0.968       0.581\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    66/499     38.7G   0.03884   0.07689 0.0005348    0.1163       516      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.947       0.936       0.962       0.565\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    67/499     38.7G   0.03889   0.07898 0.0005744    0.1185      1046      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.951       0.944       0.967       0.575\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    68/499     38.7G   0.03884   0.07877  0.000523    0.1181       457      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.49it/s]\n","                 all         999       45009       0.953       0.948       0.967       0.575\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    69/499     38.7G   0.03853   0.07913 0.0005502    0.1182       533      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.947       0.947       0.967       0.577\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    70/499     38.7G   0.03847   0.07963 0.0005549    0.1187       426      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.944       0.925       0.956       0.556\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    71/499     38.7G   0.03796   0.07654 0.0005418     0.115       490      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.949       0.944       0.966       0.575\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    72/499     38.7G   0.03802   0.07746 0.0005365     0.116       657      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.951        0.95       0.969       0.581\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    73/499     38.7G   0.03828   0.07772 0.0004932    0.1165      1014      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.953       0.947       0.969       0.585\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    74/499     38.7G   0.03861   0.07739 0.0005734    0.1166       706      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.953       0.945       0.969       0.584\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    75/499     38.7G   0.03849   0.07888 0.0005188    0.1179       530      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.954       0.944       0.968       0.579\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    76/499     38.7G   0.03766   0.07786 0.0005147     0.116       483      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.959       0.945       0.969       0.585\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    77/499     38.7G   0.03776   0.07757 0.0005154    0.1158       444      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.955       0.947       0.969       0.582\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    78/499     38.7G   0.03771   0.07499 0.0005128    0.1132       587      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.959       0.947       0.969       0.587\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    79/499     38.7G   0.03748   0.07842 0.0004929    0.1164       739      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009        0.95       0.955        0.97       0.585\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    80/499     38.7G   0.03749   0.07843 0.0005142    0.1164       711      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.957       0.946       0.969       0.583\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    81/499     38.7G   0.03785   0.07554 0.0005215    0.1139       664      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.953        0.95        0.97       0.585\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    82/499     38.7G   0.03753   0.07703 0.0005286    0.1151       755      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.956       0.951       0.971        0.59\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    83/499     38.7G   0.03757   0.07566 0.0005057    0.1137       805      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.958       0.948        0.97       0.589\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    84/499     38.7G   0.03796   0.07755 0.0004842     0.116       648      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009        0.96       0.944        0.97       0.593\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    85/499     38.7G   0.03763   0.07981 0.0004775    0.1179      1154      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.955        0.95        0.97       0.591\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    86/499     38.7G   0.03735   0.07867 0.0004892    0.1165       339      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.95       0.952       0.968       0.584\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    87/499     38.7G    0.0382   0.08059 0.0005196    0.1193       977      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.959       0.948        0.97       0.591\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    88/499     38.7G   0.03803   0.07895 0.0004613    0.1174       361      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.959       0.948        0.97       0.588\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    89/499     38.7G   0.03749   0.07643 0.0004896    0.1144       586      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.957       0.949       0.969        0.59\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    90/499     38.7G   0.03738   0.07876 0.0004849    0.1166       737      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.956       0.951        0.97       0.591\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    91/499     38.7G   0.03745   0.07574 0.0004756    0.1137       530      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.959       0.949       0.972       0.596\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    92/499     38.7G   0.03764   0.07724 0.0005138    0.1154       525      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.949       0.939       0.964       0.568\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    93/499     38.7G   0.03741   0.07821 0.0004958    0.1161       790      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.955       0.948        0.97       0.589\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    94/499     38.7G   0.03742   0.07708 0.0004912     0.115       668      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.959       0.948        0.97        0.59\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    95/499     38.7G   0.03734   0.08071 0.0004688    0.1185       967      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.956       0.953       0.971       0.596\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    96/499     38.7G   0.03775    0.0764 0.0005145    0.1147       626      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.957       0.946        0.97       0.592\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    97/499     38.7G   0.03773   0.07755 0.0005097    0.1158       763      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.958       0.953       0.972       0.591\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    98/499     38.7G   0.03735   0.07768 0.0005001    0.1155       522      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.959       0.953       0.972       0.595\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","    99/499     38.7G   0.03718   0.07513 0.0004457    0.1128       795      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.10it/s]\n","                 all         999       45009       0.965       0.951       0.974       0.599\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   100/499     38.7G   0.03774   0.07726 0.0004932    0.1155      1130      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.958       0.954       0.972       0.591\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   101/499     38.7G     0.037   0.07311   0.00045    0.1106       864      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.956       0.957       0.971       0.599\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   102/499     38.7G   0.03728   0.07747 0.0004577    0.1152       784      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.961       0.956       0.973       0.601\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   103/499     38.7G   0.03795   0.07736 0.0004996    0.1158       825      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009        0.96       0.953       0.972       0.599\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   104/499     38.7G   0.03682   0.07543  0.000476    0.1127       630      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.962       0.949       0.972         0.6\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   105/499     38.7G   0.03623   0.07705 0.0004859    0.1138       877      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.964       0.949       0.971         0.6\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   106/499     38.7G   0.03747   0.07783 0.0004296    0.1157       827      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.961       0.952       0.973       0.602\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   107/499     38.7G    0.0375   0.07671  0.000433    0.1146       977      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.961        0.95       0.971       0.596\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   108/499     38.7G   0.03687   0.07374 0.0004534    0.1111       534      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009        0.96       0.954       0.973         0.6\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   109/499     38.7G   0.03735   0.07866 0.0004313    0.1164       982      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.53it/s]\n","                 all         999       45009       0.965        0.95       0.974       0.602\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   110/499     38.7G   0.03626   0.07219 0.0004186    0.1089       444      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.964        0.95       0.973       0.599\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   111/499     38.7G   0.03747   0.07421 0.0004418    0.1121       633      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009        0.96       0.952       0.973         0.6\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   112/499     38.7G   0.03707   0.07633 0.0004676    0.1139       648      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.957        0.95       0.971       0.591\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   113/499     38.7G   0.03717   0.07844 0.0004544    0.1161       810      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.964       0.953       0.974       0.602\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   114/499     38.7G   0.03652   0.07446 0.0004253    0.1114       538      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.956       0.961       0.974       0.606\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   115/499     38.7G    0.0377   0.07616 0.0004051    0.1143       789      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.956       0.957       0.974       0.601\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   116/499     38.7G   0.03678   0.07669 0.0004199    0.1139       670      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.962       0.954       0.975       0.606\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   117/499     38.7G   0.03659   0.07497 0.0004586     0.112       622      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.961       0.954       0.974       0.604\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   118/499     38.7G   0.03724   0.07989   0.00044    0.1176       819      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:13<00:00,  1.66it/s]\n","                 all         999       45009       0.963       0.954       0.974       0.606\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   119/499     38.7G   0.03692   0.07964  0.000426     0.117      1194      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.961       0.951       0.973       0.601\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   120/499     38.7G   0.03686   0.07673 0.0004324     0.114       759      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.964       0.953       0.975       0.605\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   121/499     38.7G   0.03757   0.07694 0.0004687     0.115       603      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.958       0.954       0.973       0.602\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   122/499     38.7G   0.03667   0.07535  0.000457    0.1125       974      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009        0.96       0.954       0.973       0.598\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   123/499     38.7G   0.03662   0.07588 0.0004085    0.1129       747      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.961       0.953       0.973       0.601\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   124/499     38.7G   0.03667   0.07208 0.0004127    0.1092       600      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.963       0.955       0.974       0.607\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   125/499     38.7G   0.03625   0.07499 0.0004247    0.1117       652      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.963       0.955       0.976       0.611\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   126/499     38.7G   0.03643   0.07496 0.0004284    0.1118       789      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.96       0.954       0.974       0.604\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   127/499     38.7G   0.03715   0.07929 0.0004369    0.1169       944      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.959        0.96       0.976       0.612\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   128/499     38.7G   0.03645   0.07941 0.0004542    0.1163       936      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.965       0.952       0.974       0.608\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   129/499     38.7G   0.03627   0.07375 0.0004582    0.1105       642      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.965       0.952       0.975        0.61\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   130/499     38.7G   0.03652   0.07525 0.0004148    0.1122       925      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.955       0.952       0.971       0.593\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   131/499     38.7G   0.03679   0.07463 0.0004452    0.1119       656      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.951       0.941       0.968       0.579\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   132/499     38.7G   0.03711   0.07801 0.0004213    0.1155       912      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.956        0.95       0.973       0.601\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   133/499     38.7G   0.03673   0.07306 0.0003912    0.1102       780      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.961       0.951       0.973       0.604\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   134/499     38.7G   0.03719   0.07597 0.0004216    0.1136       759      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.966        0.95       0.974       0.606\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   135/499     38.7G   0.03704   0.07414 0.0004183    0.1116       774      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.959       0.954       0.973       0.601\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   136/499     38.7G   0.03615   0.07637 0.0004069    0.1129       689      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.49it/s]\n","                 all         999       45009       0.961       0.955       0.973       0.605\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   137/499     38.7G   0.03664   0.07773 0.0004299    0.1148      1396      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.965       0.953       0.976       0.614\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   138/499     38.7G    0.0363   0.07535 0.0003913     0.112       496      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.962       0.956       0.975       0.613\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   139/499     38.7G   0.03618   0.07516 0.0004187    0.1118       757      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.967       0.956       0.976       0.611\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   140/499     38.7G   0.03587   0.07431 0.0004051    0.1106      1015      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.966       0.953       0.976        0.61\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   141/499     38.7G   0.03629   0.07602 0.0004043    0.1127       855      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.53it/s]\n","                 all         999       45009       0.964       0.959       0.977       0.612\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   142/499     38.7G   0.03577   0.07172 0.0004266    0.1079       542      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.966       0.955       0.977       0.614\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   143/499     38.7G   0.03636   0.07783  0.000385    0.1146       736      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.967       0.956       0.977       0.617\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   144/499     38.7G   0.03598   0.07693 0.0003953    0.1133      1009      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.48it/s]\n","                 all         999       45009       0.962       0.957       0.976       0.612\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   145/499     38.7G   0.03595    0.0735 0.0004156    0.1099       663      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.966       0.957       0.977       0.617\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   146/499     38.7G   0.03625   0.07646 0.0003925    0.1131       549      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.966       0.953       0.976       0.612\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   147/499     38.7G   0.03585   0.07332 0.0004174    0.1096       773      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.964       0.957       0.977       0.615\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   148/499     38.7G   0.03639   0.07659 0.0004226    0.1134       909      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009        0.96       0.954       0.975       0.605\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   149/499     38.7G   0.03577   0.07223 0.0003992    0.1084       515      1280: 100% 46/46 [00:59<00:00,  1.28s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.13it/s]\n","                 all         999       45009       0.963       0.956       0.976       0.614\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   150/499     38.7G   0.03582   0.07524 0.0003897    0.1114       633      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.965       0.955       0.976       0.616\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   151/499     38.7G   0.03604   0.07616 0.0003958    0.1126       790      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.966       0.957       0.977       0.618\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   152/499     38.7G   0.03561   0.07318 0.0004061    0.1092       887      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.969       0.955       0.977       0.618\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   153/499     38.7G   0.03584    0.0733 0.0003786    0.1095       667      1280: 100% 46/46 [00:59<00:00,  1.28s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.968       0.955       0.977       0.617\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   154/499     38.7G    0.0356   0.07341 0.0004255    0.1094       711      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.969       0.954       0.978       0.622\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   155/499     38.7G   0.03594   0.07668 0.0004237     0.113       901      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.967       0.956       0.978       0.623\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   156/499     38.7G   0.03546   0.07329  0.000415    0.1092       447      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.967       0.959       0.978       0.621\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   157/499     38.7G   0.03535   0.07188 0.0003821    0.1076       712      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.967       0.958       0.978       0.616\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   158/499     38.7G   0.03535   0.07455 0.0003614    0.1103       484      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.966       0.956       0.976       0.614\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   159/499     38.7G   0.03599   0.07661 0.0003857     0.113       786      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.967       0.961       0.977       0.622\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   160/499     38.7G   0.03589   0.07845 0.0004038    0.1147       955      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.971       0.957       0.979       0.622\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   161/499     38.7G    0.0355   0.07432 0.0003733    0.1102       606      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.968       0.959       0.978       0.624\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   162/499     38.7G   0.03592   0.07091 0.0003627    0.1072       564      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.965       0.962       0.978       0.622\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   163/499     38.7G   0.03543   0.07387  0.000342    0.1096       494      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.971        0.96        0.98       0.627\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   164/499     38.7G   0.03553   0.07685  0.000366    0.1127       312      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.969       0.957       0.977       0.622\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   165/499     38.7G   0.03524   0.07323 0.0003767    0.1088       444      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.967       0.958       0.977       0.621\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   166/499     38.7G   0.03522   0.07547 0.0003355     0.111       754      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.969       0.961       0.979       0.626\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   167/499     38.7G   0.03492   0.07671 0.0004079     0.112       654      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.53it/s]\n","                 all         999       45009       0.968       0.961       0.979       0.627\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   168/499     38.7G   0.03539   0.07652 0.0003766    0.1123      1080      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.969       0.963        0.98       0.626\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   169/499     38.7G   0.03573   0.07781 0.0003895    0.1139      1066      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.969       0.962       0.979       0.622\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   170/499     38.7G   0.03554   0.07306  0.000377     0.109       482      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.971       0.959       0.978       0.625\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   171/499     38.7G   0.03532   0.07412 0.0003789    0.1098       892      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.969        0.96       0.978       0.624\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   172/499     38.7G   0.03479   0.06907 0.0003851    0.1042       718      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.969       0.961       0.979       0.628\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   173/499     38.7G   0.03559   0.07403  0.000358      0.11       689      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.968        0.96       0.979       0.624\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   174/499     38.7G   0.03545   0.07413 0.0003549    0.1099       767      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.973        0.96        0.98       0.628\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   175/499     38.7G   0.03508   0.07196 0.0003704    0.1074       786      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.968       0.963       0.979        0.63\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   176/499     38.7G   0.03515   0.07136 0.0003745    0.1069       538      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.969       0.961       0.978       0.625\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   177/499     38.7G   0.03521   0.07426 0.0003772    0.1098       592      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.965       0.966       0.979        0.63\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   178/499     38.7G   0.03535   0.07361 0.0003589    0.1093       902      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.968       0.963       0.979       0.629\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   179/499     38.7G    0.0346   0.07328 0.0003514    0.1082       618      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009        0.97       0.962       0.979       0.631\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   180/499     38.7G   0.03461   0.07271 0.0003344    0.1077       717      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.968       0.963       0.979       0.631\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   181/499     38.7G   0.03504   0.07573 0.0003768    0.1111       683      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.965       0.966       0.979       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   182/499     38.7G   0.03493   0.07127 0.0003388    0.1065       796      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009        0.97       0.962       0.981       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   183/499     38.7G   0.03533   0.07416 0.0003935    0.1099       350      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.50it/s]\n","                 all         999       45009       0.965       0.963       0.978        0.63\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   184/499     38.7G   0.03528   0.07394 0.0003602    0.1096       458      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.97        0.96       0.979        0.63\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   185/499     38.7G   0.03505   0.07363 0.0003496     0.109      1059      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.97        0.96        0.98        0.63\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   186/499     38.7G   0.03514   0.07526 0.0003439    0.1107       591      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.968       0.963        0.98       0.632\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   187/499     38.7G   0.03512   0.07175 0.0003362    0.1072       321      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.53it/s]\n","                 all         999       45009       0.963       0.967       0.979       0.634\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   188/499     38.7G   0.03492   0.07598  0.000337    0.1112       457      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.973       0.959       0.979       0.632\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   189/499     38.7G   0.03457   0.07234 0.0003618    0.1073       600      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.968       0.964       0.979       0.632\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   190/499     38.7G   0.03484   0.07528 0.0003563    0.1105       630      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.969       0.962       0.978       0.628\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   191/499     38.7G   0.03495   0.07213 0.0003604    0.1074       550      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.973       0.961        0.98       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   192/499     38.7G   0.03508   0.07317 0.0003364    0.1086       644      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.45it/s]\n","                 all         999       45009       0.969       0.962       0.979       0.626\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   193/499     38.7G   0.03601   0.07995 0.0003644    0.1163      1134      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009        0.97       0.958       0.979       0.626\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   194/499     38.7G   0.03543   0.07289 0.0003354    0.1087       608      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.968       0.964       0.979       0.632\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   195/499     38.7G   0.03508    0.0738 0.0003565    0.1092       670      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.969       0.958       0.978       0.628\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   196/499     38.7G   0.03556   0.07625 0.0003584    0.1122       745      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.973       0.958       0.979       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   197/499     38.7G   0.03568   0.07394 0.0003373      0.11       546      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.973       0.959       0.979       0.632\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   198/499     38.7G   0.03485   0.07426 0.0003459    0.1095       966      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.971        0.96       0.979       0.636\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   199/499     38.7G   0.03522   0.07325 0.0003348    0.1088       874      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.12it/s]\n","                 all         999       45009       0.969       0.963       0.979       0.634\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   200/499     38.7G   0.03486   0.07356 0.0003477    0.1088       807      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.968       0.963       0.979       0.635\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   201/499     38.7G   0.03489   0.07219 0.0003329    0.1074       502      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.971       0.963        0.98       0.637\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   202/499     38.7G   0.03495    0.0753 0.0003594    0.1106       837      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.971       0.961       0.979       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   203/499     38.7G    0.0355   0.07203 0.0003456    0.1079       806      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.971       0.963        0.98       0.639\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   204/499     38.7G   0.03435   0.07369 0.0003484    0.1084       810      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009        0.97       0.965        0.98       0.635\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   205/499     38.7G    0.0347   0.07266 0.0003435    0.1077       667      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.971        0.96       0.979       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   206/499     38.7G   0.03508   0.07367 0.0003402    0.1091       710      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.97       0.966        0.98       0.639\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   207/499     38.7G   0.03465   0.07138 0.0003522    0.1064       668      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.974       0.962       0.981       0.635\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   208/499     38.7G   0.03665   0.07455 0.0003933    0.1116       578      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.96       0.955       0.975        0.61\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   209/499     38.7G   0.03653   0.07865 0.0003708    0.1155       550      1280: 100% 46/46 [01:02<00:00,  1.35s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.967       0.954       0.978       0.623\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   210/499     38.7G   0.03549   0.07457 0.0003391    0.1104       837      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.971       0.955       0.979       0.627\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   211/499     38.7G   0.03498   0.07246 0.0003466    0.1078       830      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.969       0.961       0.979       0.629\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   212/499     38.7G   0.03507   0.07313 0.0003475    0.1086       367      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.971       0.963        0.98       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   213/499     38.7G   0.03463   0.07386 0.0003367    0.1088       551      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.972       0.962        0.98       0.636\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   214/499     38.7G   0.03472   0.07425 0.0003252    0.1093       643      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.971       0.964       0.981       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   215/499     38.7G   0.03426   0.07263  0.000335    0.1072       893      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.973       0.961       0.981       0.638\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   216/499     38.7G   0.03425   0.07211 0.0003203    0.1067       900      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.974       0.962       0.981       0.638\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   217/499     38.7G   0.03506   0.07166 0.0003017     0.107       656      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.973       0.965        0.98       0.638\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   218/499     38.7G   0.03462   0.07313 0.0003345    0.1081       779      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.973       0.964       0.981        0.64\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   219/499     38.7G   0.03453   0.07267 0.0003193    0.1075       625      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.971       0.966       0.982       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   220/499     38.7G   0.03476   0.07567 0.0003252    0.1108       742      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.974       0.965       0.981       0.643\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   221/499     38.7G   0.03443   0.07254 0.0003203    0.1073       912      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.973       0.965       0.982       0.644\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   222/499     38.7G   0.03435   0.07302 0.0003271    0.1077       596      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.972       0.966       0.981       0.646\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   223/499     38.7G   0.03421   0.07484 0.0003347    0.1094       498      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.975       0.963       0.981       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   224/499     38.7G   0.03425   0.07274 0.0003502    0.1073       931      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.971       0.963       0.981        0.64\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   225/499     38.7G   0.03483    0.0723 0.0003199    0.1075       679      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.975       0.964       0.982       0.642\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   226/499     38.7G   0.03445   0.07263 0.0003013    0.1074       485      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.974       0.964       0.982       0.643\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   227/499     38.7G   0.03445   0.07413 0.0003408    0.1089       740      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.64it/s]\n","                 all         999       45009        0.97       0.965       0.981       0.639\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   228/499     38.7G   0.03453   0.07358 0.0002982    0.1084       575      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.971       0.966       0.981       0.642\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   229/499     38.7G   0.03426   0.06974 0.0003507    0.1044       536      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.968       0.968       0.981       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   230/499     38.7G    0.0347   0.07445 0.0003462    0.1095       930      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.974       0.962       0.981       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   231/499     38.7G   0.03436    0.0717 0.0003217    0.1064       519      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.971       0.963       0.981       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   232/499     38.7G   0.03414   0.07013 0.0003393    0.1046       530      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.973       0.962       0.981       0.639\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   233/499     38.7G   0.03437   0.07219 0.0002999    0.1069       523      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.976        0.96       0.981       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   234/499     38.7G   0.03368   0.06872 0.0003092    0.1027       521      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.971       0.966       0.982       0.645\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   235/499     38.7G   0.03399   0.07291 0.0003144    0.1072       620      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.974       0.964       0.982       0.646\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   236/499     38.7G    0.0336   0.07198 0.0002926    0.1059       706      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.976       0.964       0.983       0.647\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   237/499     38.7G   0.03339   0.06976 0.0003101    0.1035       352      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.974       0.966       0.983       0.644\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   238/499     38.7G   0.03427   0.07204 0.0002943    0.1066       907      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.974       0.966       0.983       0.647\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   239/499     38.7G   0.03359   0.06997 0.0003369    0.1039       634      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.976       0.964       0.983       0.648\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   240/499     38.7G   0.03358   0.06842 0.0003128    0.1023       732      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.972       0.968       0.982       0.647\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   241/499     38.7G   0.03346   0.06721 0.0003071     0.101       326      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.974       0.964       0.983       0.648\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   242/499     38.7G   0.03383    0.0742 0.0003173    0.1083       654      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.973       0.967       0.983       0.651\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   243/499     38.7G   0.03364   0.07282 0.0002992    0.1068       912      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.976       0.963       0.982       0.651\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   244/499     38.7G   0.03388   0.07476 0.0003094     0.109      1103      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.975       0.966       0.983       0.651\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   245/499     38.7G   0.03308   0.07069 0.0002926    0.1041      1005      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.973       0.967       0.983       0.649\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   246/499     38.7G   0.03336   0.06846 0.0002987    0.1021       417      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.975       0.965       0.983        0.65\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   247/499     38.7G    0.0335   0.07153  0.000279    0.1053       784      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.972       0.967       0.983       0.649\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   248/499     38.7G   0.03353   0.07142 0.0003036    0.1052       613      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009        0.97        0.97       0.983       0.653\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   249/499     38.7G   0.03406   0.07168 0.0003016     0.106      1120      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.12it/s]\n","                 all         999       45009       0.974        0.97       0.984       0.652\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   250/499     38.7G   0.03398   0.07325 0.0003079    0.1075       744      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.973       0.969       0.984        0.65\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   251/499     38.7G   0.03409   0.07744  0.000288    0.1118       875      1280: 100% 46/46 [01:02<00:00,  1.35s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.976       0.966       0.983       0.652\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   252/499     38.7G   0.03443   0.07394  0.000323    0.1087       952      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.967       0.963        0.98       0.636\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   253/499     38.7G   0.03387   0.06917 0.0002906    0.1033       475      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.974       0.961       0.981       0.643\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   254/499     38.7G    0.0336    0.0708 0.0003221    0.1047       745      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.977       0.963       0.981       0.649\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   255/499     38.7G   0.03386   0.07096 0.0002835    0.1051       733      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.975       0.967       0.983       0.653\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   256/499     38.7G   0.03488   0.07522 0.0003598    0.1105       977      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.963       0.944       0.965       0.622\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   257/499     38.7G   0.03655   0.07583 0.0003738    0.1128       709      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.957       0.947       0.966       0.614\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   258/499     38.7G   0.03524   0.07265 0.0003495    0.1082       725      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.965       0.947        0.97       0.628\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   259/499     38.7G   0.03486   0.07019 0.0003321    0.1054       636      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.968       0.949       0.972       0.633\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   260/499     38.7G   0.03471   0.07246 0.0003195    0.1075      1034      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.969       0.954       0.975       0.641\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   261/499     38.7G   0.03411   0.06975 0.0003169    0.1042       745      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.97       0.956       0.976       0.644\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   262/499     38.7G   0.03441   0.07221 0.0003056    0.1069       692      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009        0.97       0.959       0.978       0.644\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   263/499     38.7G   0.03356   0.07035  0.000284    0.1042       958      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.97       0.962       0.979       0.646\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   264/499     38.7G   0.03432   0.07365 0.0002953    0.1083       948      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.64it/s]\n","                 all         999       45009       0.971       0.961       0.979        0.65\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   265/499     38.7G   0.03368   0.07127 0.0002889    0.1052       820      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.974        0.96        0.98        0.65\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   266/499     38.7G   0.03361   0.07193 0.0002987    0.1058       559      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.973       0.961        0.98       0.648\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   267/499     38.7G     0.034    0.0699  0.000315    0.1042       681      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.971       0.964        0.98       0.651\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   268/499     38.7G   0.03446   0.07103 0.0002842    0.1058       517      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.974       0.961        0.98       0.649\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   269/499     38.7G   0.03375    0.0695 0.0002787    0.1035       621      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.971       0.965        0.98       0.651\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   270/499     38.7G   0.03391   0.07208 0.0003267    0.1063       549      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.972       0.963        0.98       0.651\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   271/499     38.7G   0.03361   0.06854 0.0002896    0.1024       436      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.971       0.965       0.981       0.649\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   272/499     38.7G    0.0342   0.07004 0.0002814    0.1045       663      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:13<00:00,  1.64it/s]\n","                 all         999       45009       0.973       0.964       0.981       0.653\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   273/499     38.7G    0.0335   0.07033 0.0002878    0.1041       529      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.974       0.964       0.981       0.655\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   274/499     38.7G   0.03362   0.06727 0.0002953    0.1012       857      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.975       0.963        0.98       0.654\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   275/499     38.7G   0.03371   0.07042 0.0002934    0.1044       966      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.976       0.963       0.982       0.653\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   276/499     38.7G   0.03346   0.07204 0.0002696    0.1058       391      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.979       0.959       0.982       0.655\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   277/499     38.7G   0.03351    0.0708 0.0002894    0.1046      1088      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.977       0.962       0.982       0.657\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   278/499     38.7G   0.03367   0.07481 0.0002632    0.1087       919      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.975       0.966       0.982       0.655\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   279/499     38.7G   0.03391   0.06972 0.0002871    0.1039       574      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.975       0.963       0.982       0.653\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   280/499     38.7G   0.03358   0.07028 0.0002715    0.1041       731      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.973       0.966       0.982       0.655\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   281/499     38.7G   0.03325   0.07034 0.0002709    0.1039       615      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.974       0.966       0.983       0.657\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   282/499     38.7G   0.03337    0.0718 0.0002636    0.1054       999      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.973       0.967       0.982       0.655\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   283/499     38.7G   0.03362    0.0731 0.0002825     0.107       518      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.975       0.966       0.982       0.658\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   284/499     38.7G   0.03348   0.06964 0.0002907    0.1034       900      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.976       0.967       0.983       0.658\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   285/499     38.7G   0.03342   0.06963 0.0002569    0.1033       878      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.977       0.967       0.984        0.66\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   286/499     38.7G   0.03342   0.07126 0.0003019     0.105       774      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.976       0.965       0.983       0.659\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   287/499     38.7G    0.0337    0.0701 0.0002615    0.1041       582      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.978       0.964       0.983       0.658\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   288/499     38.7G   0.03341   0.07114 0.0002774    0.1048       732      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.973       0.968       0.984       0.656\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   289/499     38.7G   0.03317   0.07181 0.0002703    0.1053       878      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.975       0.968       0.984       0.658\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   290/499     38.7G   0.03327   0.06989 0.0002788    0.1034       559      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.973        0.97       0.984       0.659\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   291/499     38.7G   0.03297   0.07016 0.0002684    0.1034       819      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.977       0.967       0.984       0.662\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   292/499     38.7G   0.03298   0.06787 0.0002566    0.1011       385      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.977       0.968       0.984       0.662\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   293/499     38.7G   0.03342   0.07051 0.0002742    0.1042       558      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.977       0.969       0.984       0.662\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   294/499     38.7G   0.03392   0.06791 0.0002627    0.1021       484      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.976       0.969       0.984       0.661\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   295/499     38.7G   0.03335   0.06835 0.0002707     0.102       931      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.976       0.967       0.983        0.66\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   296/499     38.7G   0.03296   0.06848 0.0002915    0.1017       778      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.976       0.968       0.984       0.662\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   297/499     38.7G   0.03277   0.07036 0.0002608    0.1034       562      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.979       0.966       0.985       0.663\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   298/499     38.7G   0.03285   0.07137 0.0002715    0.1045       838      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.978       0.968       0.985       0.664\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   299/499     38.7G   0.03296   0.07158 0.0002687    0.1048       655      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.14it/s]\n","                 all         999       45009       0.978       0.968       0.985       0.664\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   300/499     38.7G   0.03309   0.07104 0.0002746    0.1044       397      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.976       0.969       0.985        0.66\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   301/499     38.7G   0.03283      0.07 0.0002631    0.1031      1105      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.976        0.97       0.985       0.663\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   302/499     38.7G   0.03271    0.0687 0.0002603    0.1017       811      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.976        0.97       0.985       0.666\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   303/499     38.7G   0.03299   0.06858 0.0002445    0.1018       637      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.975        0.97       0.984       0.665\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   304/499     38.7G   0.03275   0.06733 0.0002472    0.1003       827      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.975       0.968       0.985       0.662\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   305/499     38.7G   0.03274   0.06855 0.0002792    0.1016       672      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.49it/s]\n","                 all         999       45009       0.979       0.965       0.985       0.663\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   306/499     38.7G   0.03283    0.0701 0.0002809    0.1032       678      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.977       0.968       0.984       0.665\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   307/499     38.7G   0.03361   0.06955 0.0002833    0.1034       559      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.979       0.967       0.985       0.665\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   308/499     38.7G   0.03326   0.07085 0.0002694    0.1044       340      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.976        0.97       0.985       0.665\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   309/499     38.7G   0.03241   0.06449 0.0002496   0.09715       681      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.976       0.969       0.985       0.664\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   310/499     38.7G   0.03289   0.06694  0.000252    0.1001       440      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.976        0.97       0.985       0.664\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   311/499     38.7G   0.03361   0.07131 0.0002712    0.1052       710      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.977       0.968       0.985       0.663\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   312/499     38.7G   0.03311   0.06908 0.0002588    0.1024       696      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.978       0.969       0.985       0.662\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   313/499     38.7G   0.03293   0.06949 0.0002562    0.1027       582      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.979       0.967       0.984       0.663\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   314/499     38.7G   0.03247   0.06914 0.0002677    0.1019       542      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.979       0.969       0.985       0.666\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   315/499     38.7G   0.03266   0.06965 0.0002832    0.1026       709      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.98       0.968       0.985       0.668\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   316/499     38.7G   0.03222   0.06713 0.0002486    0.0996       395      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.978       0.969       0.985       0.669\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   317/499     38.7G   0.03268   0.06911 0.0002516     0.102       661      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.979        0.97       0.985       0.668\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   318/499     38.7G   0.03203   0.06843  0.000259    0.1007       276      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009        0.98       0.968       0.985        0.67\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   319/499     38.7G   0.03241   0.06856 0.0002601    0.1012       666      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.977       0.971       0.985       0.669\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   320/499     38.7G   0.03266   0.06879 0.0002435    0.1017       542      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.978       0.971       0.985       0.672\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   321/499     38.7G   0.03269   0.06907 0.0002641     0.102       732      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009        0.98       0.969       0.986        0.67\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   322/499     38.7G   0.03254   0.06771 0.0002349    0.1005       695      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.53it/s]\n","                 all         999       45009       0.975       0.972       0.985       0.669\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   323/499     38.7G   0.03251   0.06742 0.0002607    0.1002       603      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.979        0.97       0.985       0.669\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   324/499     38.7G   0.03269    0.0682 0.0002545    0.1012       622      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.977       0.971       0.986       0.668\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   325/499     38.7G   0.03246   0.06664 0.0002426   0.09934       535      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.978       0.971       0.985       0.669\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   326/499     38.7G   0.03275   0.06613 0.0002893   0.09918       541      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.978        0.97       0.985       0.668\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   327/499     38.7G   0.03253   0.06595 0.0002735   0.09875       655      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.979        0.97       0.986       0.668\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   328/499     38.7G   0.03247   0.06919 0.0002346    0.1019       813      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.976       0.973       0.986        0.67\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   329/499     38.7G   0.03255   0.06939  0.000243    0.1022       492      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.978       0.971       0.987        0.67\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   330/499     38.7G    0.0328   0.06786 0.0002374    0.1009      1128      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009        0.98        0.97       0.986        0.67\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   331/499     38.7G   0.03359   0.06854 0.0002784    0.1024       570      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.977       0.968       0.985       0.666\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   332/499     38.7G   0.03319   0.06809 0.0002469    0.1015      1000      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.973       0.972       0.985       0.669\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   333/499     38.7G    0.0331   0.06971 0.0002575    0.1031      1129      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.977       0.971       0.986       0.671\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   334/499     38.7G   0.03277   0.06742 0.0002736    0.1005       794      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.977       0.972       0.986       0.671\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   335/499     38.7G   0.03245   0.06967 0.0002647    0.1024       877      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.977       0.972       0.986        0.67\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   336/499     38.7G   0.03263   0.06774 0.0002644    0.1006       772      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.981       0.968       0.986       0.671\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   337/499     38.7G    0.0324   0.06731 0.0002482   0.09995       664      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009        0.98        0.97       0.986       0.674\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   338/499     38.7G   0.03286   0.06758 0.0002426    0.1007       614      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.981       0.969       0.986       0.674\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   339/499     38.7G    0.0325   0.06833 0.0002626    0.1011      1173      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.981        0.97       0.986       0.677\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   340/499     38.7G   0.03266   0.06867 0.0002998    0.1016      1049      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.981       0.969       0.986       0.673\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   341/499     38.7G    0.0331   0.06662 0.0002542   0.09998       622      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.981       0.969       0.986       0.674\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   342/499     38.7G   0.03263   0.06964  0.000245    0.1025      1051      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.978       0.972       0.986       0.672\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   343/499     38.7G    0.0322   0.06682 0.0002676   0.09929       735      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009        0.98        0.97       0.986       0.674\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   344/499     38.7G    0.0323    0.0702 0.0002548    0.1028       720      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.981       0.969       0.986       0.673\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   345/499     38.7G   0.03232   0.06664  0.000236    0.0992       642      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.979       0.971       0.986       0.674\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   346/499     38.7G    0.0321   0.06752  0.000237   0.09986       653      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009        0.98       0.971       0.986       0.675\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   347/499     38.7G   0.03182   0.06745 0.0002495   0.09951       772      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.977       0.973       0.986       0.675\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   348/499     38.7G   0.03229   0.06631  0.000277   0.09888       454      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982       0.967       0.986       0.677\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   349/499     38.7G   0.03232   0.07056 0.0002703    0.1031       950      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.14it/s]\n","                 all         999       45009       0.981       0.968       0.986       0.675\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   350/499     38.7G   0.03232   0.06564 0.0002566   0.09821       763      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.982       0.968       0.986       0.674\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   351/499     38.7G   0.03207   0.06615 0.0002551   0.09848      1023      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009        0.98       0.969       0.986       0.676\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   352/499     38.7G   0.03216   0.06883 0.0002495    0.1012       835      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.977       0.972       0.986       0.678\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   353/499     38.7G   0.03252   0.07026 0.0002607     0.103       899      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.978       0.973       0.986       0.675\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   354/499     38.7G   0.03266   0.06461   0.00025   0.09752       535      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.979       0.972       0.987       0.675\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   355/499     38.7G    0.0325   0.06928 0.0002529     0.102       468      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.49it/s]\n","                 all         999       45009        0.98       0.971       0.987       0.677\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   356/499     38.7G   0.03298    0.0679 0.0002714    0.1012       440      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.981       0.971       0.986       0.676\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   357/499     38.7G   0.03295   0.06815 0.0002644    0.1014       442      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.979       0.971       0.986       0.673\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   358/499     38.7G   0.03252   0.06677 0.0002574   0.09954       792      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009        0.98       0.971       0.986       0.678\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   359/499     38.7G   0.03249     0.068 0.0002603    0.1008       760      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.979       0.972       0.987       0.677\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   360/499     38.7G   0.03235   0.06497 0.0002464   0.09756       766      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.981       0.971       0.986       0.678\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   361/499     38.7G    0.0321   0.06648 0.0002283   0.09881       416      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.983        0.97       0.987       0.678\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   362/499     38.7G   0.03191   0.06696 0.0002617   0.09913       471      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.981        0.97       0.987       0.677\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   363/499     38.7G   0.03184   0.06526 0.0002319   0.09733       586      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.48it/s]\n","                 all         999       45009       0.979       0.973       0.987       0.679\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   364/499     38.7G   0.03209   0.06954 0.0002445    0.1019       771      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.979       0.973       0.986        0.68\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   365/499     38.7G   0.03212   0.06655 0.0002503   0.09892       759      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009        0.98       0.972       0.986       0.682\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   366/499     38.7G   0.03208   0.07023 0.0002357    0.1025       796      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.98       0.972       0.986       0.681\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   367/499     38.7G   0.03209    0.0692 0.0002478    0.1015       884      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.978       0.974       0.986        0.68\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   368/499     38.7G   0.03219   0.06938 0.0002335    0.1018       858      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.982       0.971       0.986       0.677\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   369/499     38.7G   0.03173    0.0678 0.0002661   0.09979       543      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.981       0.972       0.987       0.678\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   370/499     38.7G   0.03167   0.06552 0.0002437   0.09743       784      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.981       0.972       0.987        0.68\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   371/499     38.7G   0.03176   0.06757  0.000252   0.09958       631      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.982        0.97       0.987        0.68\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   372/499     38.7G   0.03153   0.06633   0.00025   0.09811      1142      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.983       0.971       0.987       0.679\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   373/499     38.7G   0.03169   0.06823 0.0002567    0.1002       523      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.982       0.971       0.987        0.68\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   374/499     38.7G   0.03177   0.06424   0.00023   0.09624       915      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009        0.98       0.973       0.987       0.681\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   375/499     38.7G   0.03147   0.06685 0.0002395   0.09856       585      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.98       0.973       0.987       0.682\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   376/499     38.7G   0.03163    0.0666 0.0002286   0.09846       782      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.981       0.971       0.987       0.683\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   377/499     38.7G   0.03148   0.06647  0.000224   0.09818       798      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.982        0.97       0.987       0.684\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   378/499     38.7G   0.03157   0.06816 0.0002368   0.09997       926      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.979       0.974       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   379/499     38.7G   0.03172   0.06738 0.0002206   0.09933       960      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.982       0.972       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   380/499     38.7G    0.0315   0.06378 0.0002172    0.0955       816      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.981       0.972       0.987       0.684\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   381/499     38.7G   0.03178   0.06681 0.0002306   0.09882       537      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009        0.98       0.972       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   382/499     38.7G   0.03123   0.06221 0.0002336   0.09367       546      1280: 100% 46/46 [00:58<00:00,  1.28s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009        0.98       0.972       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   383/499     38.7G   0.03172   0.06493 0.0002285   0.09688       516      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.977       0.975       0.987       0.686\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   384/499     38.7G   0.03144   0.06346 0.0002209   0.09512       667      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982        0.97       0.987       0.686\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   385/499     38.7G   0.03115    0.0645 0.0002305   0.09588       410      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.979       0.972       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   386/499     38.7G   0.03196   0.06846 0.0002477    0.1007       767      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009        0.98       0.973       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   387/499     38.7G   0.03195   0.06989 0.0002347    0.1021       635      1280: 100% 46/46 [01:02<00:00,  1.35s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.98       0.972       0.987       0.684\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   388/499     38.7G   0.03163   0.06536 0.0002227   0.09722       391      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.979       0.973       0.987       0.684\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   389/499     38.7G   0.03159   0.06531 0.0002141   0.09711       688      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.979       0.973       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   390/499     38.7G   0.03131   0.06357 0.0002133   0.09509       527      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.984       0.969       0.987       0.686\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   391/499     38.7G   0.03094   0.06475 0.0002435   0.09594       801      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.978       0.976       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   392/499     38.7G   0.03174   0.06802 0.0002203   0.09998       692      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.981       0.973       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   393/499     38.7G   0.03167   0.06416 0.0002352   0.09607       735      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.979       0.973       0.987       0.684\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   394/499     38.7G   0.03161   0.07095 0.0002509    0.1028      1045      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.978       0.974       0.987       0.683\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   395/499     38.7G   0.03146   0.06374 0.0002616   0.09546       746      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.978       0.976       0.987       0.685\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   396/499     38.7G   0.03136   0.06626 0.0002283   0.09784      1056      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.983       0.973       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   397/499     38.7G   0.03081   0.06406 0.0002451   0.09512       832      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009        0.98       0.975       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   398/499     38.7G   0.03104   0.06607 0.0002246   0.09734       697      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.982       0.972       0.987       0.687\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   399/499     38.7G   0.03117   0.06723 0.0002306   0.09863       656      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.14it/s]\n","                 all         999       45009       0.983       0.971       0.987       0.687\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   400/499     38.7G   0.03146   0.06505 0.0002506   0.09675       732      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.982       0.971       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   401/499     38.7G   0.03183   0.06778 0.0002347   0.09984      1015      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.979       0.974       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   402/499     38.7G   0.03123   0.06713 0.0002302   0.09859       470      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.979       0.975       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   403/499     38.7G   0.03177   0.06884   0.00025    0.1009       651      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.982       0.971       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   404/499     38.7G   0.03097   0.06607 0.0002041   0.09725       668      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.979       0.973       0.987       0.687\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   405/499     38.7G   0.03074   0.06266 0.0002392   0.09364       355      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.979       0.974       0.987       0.687\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   406/499     38.7G   0.03112   0.06222 0.0002115   0.09356       364      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.979       0.974       0.987       0.689\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   407/499     38.7G   0.03169   0.06775 0.0002298   0.09966       630      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009        0.98       0.973       0.987       0.687\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   408/499     38.7G   0.03129   0.06552 0.0002296   0.09703       642      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.98       0.974       0.987       0.687\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   409/499     38.7G   0.03116   0.06573 0.0002232   0.09711       675      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.979       0.974       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   410/499     38.7G   0.03132   0.06515 0.0002314    0.0967       736      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009        0.98       0.975       0.988       0.689\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   411/499     38.7G   0.03126   0.06712 0.0002414   0.09862       639      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.982       0.973       0.988       0.689\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   412/499     38.7G   0.03145   0.06591 0.0002332    0.0976       980      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.982       0.974       0.988        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   413/499     38.7G   0.03112   0.06582 0.0002373   0.09718       692      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.981       0.974       0.988       0.689\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   414/499     38.7G   0.03132   0.06612 0.0002468   0.09769       486      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.981       0.974       0.988       0.689\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   415/499     38.7G   0.03088    0.0635 0.0002017   0.09458       653      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.982       0.974       0.988       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   416/499     38.7G   0.03046   0.06368 0.0002145   0.09435       714      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.982       0.974       0.988       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   417/499     38.7G   0.03101   0.06284 0.0002064   0.09406       576      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.982       0.974       0.988       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   418/499     38.7G   0.03137   0.06885  0.000228    0.1004       917      1280: 100% 46/46 [01:02<00:00,  1.35s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.982       0.973       0.988        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   419/499     38.7G   0.03149   0.06402 0.0002167   0.09573       524      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009        0.98       0.974       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   420/499     38.7G   0.03118   0.06726 0.0002315   0.09867       523      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009        0.98       0.974       0.987        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   421/499     38.7G   0.03072   0.06453  0.000209   0.09546       481      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982       0.974       0.987        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   422/499     38.7G     0.031   0.06852  0.000222   0.09975       794      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.981       0.975       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   423/499     38.7G    0.0311   0.06506 0.0002495   0.09642      1167      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009        0.98       0.976       0.987        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   424/499     38.7G   0.03091   0.06488 0.0002126     0.096       863      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.983       0.973       0.987        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   425/499     38.7G   0.03095   0.06438 0.0002272   0.09556       440      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.983       0.973       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   426/499     38.7G   0.03099   0.06434 0.0002161   0.09554       512      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.981       0.973       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   427/499     38.7G   0.03122   0.06367 0.0002316   0.09512       782      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.982       0.972       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   428/499     38.7G   0.03123   0.06726 0.0002104   0.09869       721      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.982       0.974       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   429/499     38.7G   0.03101   0.06877  0.000218       0.1      1157      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.982       0.974       0.987       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   430/499     38.7G   0.03141    0.0659 0.0002448   0.09755       733      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.983       0.974       0.988       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   431/499     38.7G   0.03164   0.06737 0.0002306   0.09924       614      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982       0.973       0.987       0.689\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   432/499     38.7G   0.03151   0.06535 0.0002034   0.09707       547      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.981       0.974       0.987       0.688\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   433/499     38.7G   0.03174   0.06734 0.0002314   0.09931       673      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.982       0.974       0.987        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   434/499     38.7G   0.03156   0.07057 0.0002537    0.1024       823      1280: 100% 46/46 [01:02<00:00,  1.35s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.981       0.975       0.987       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   435/499     38.7G   0.03127   0.06579 0.0002307   0.09729       530      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.981       0.975       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   436/499     38.7G   0.03098   0.06368 0.0002252   0.09489       383      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.983       0.973       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   437/499     38.7G   0.03093   0.06325 0.0002232   0.09441       583      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.982       0.974       0.987       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   438/499     38.7G   0.03124    0.0641 0.0002468   0.09558       663      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.982       0.974       0.987       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   439/499     38.7G   0.03142   0.06415 0.0002173   0.09579       621      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.981       0.974       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   440/499     38.7G   0.03086   0.06057 0.0002125   0.09165       576      1280: 100% 46/46 [00:58<00:00,  1.28s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982       0.974       0.987       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   441/499     38.7G   0.03141   0.06651 0.0002346   0.09815       803      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.982       0.973       0.987       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   442/499     38.7G   0.03142   0.06631 0.0002408   0.09798       900      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.981       0.973       0.987       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   443/499     38.7G   0.03134   0.06439 0.0002366   0.09596       714      1280: 100% 46/46 [01:00<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.979       0.976       0.987       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   444/499     38.7G   0.03162   0.06458 0.0002253   0.09643       536      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.982       0.972       0.987       0.689\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   445/499     38.7G   0.03133   0.06512 0.0002353   0.09669       414      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.981       0.975       0.988        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   446/499     38.7G   0.03138   0.06273 0.0002196   0.09433       449      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.98       0.976       0.988        0.69\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   447/499     38.7G   0.03153   0.06642 0.0002341   0.09819       710      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.983       0.974       0.988       0.691\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   448/499     38.7G   0.03092   0.06522 0.0002167   0.09635       353      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.983       0.974       0.988       0.692\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   449/499     38.7G   0.03081   0.06636 0.0002332    0.0974      1028      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:20<00:00,  1.15it/s]\n","                 all         999       45009       0.983       0.975       0.988       0.693\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   450/499     38.7G     0.031   0.06419 0.0002471   0.09544      1033      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.982       0.976       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   451/499     38.7G   0.03075   0.06629 0.0002343   0.09728       764      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.982       0.976       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   452/499     38.7G   0.03084   0.06436 0.0002054   0.09541       476      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.981       0.976       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   453/499     38.7G     0.031    0.0658 0.0002334   0.09703       519      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982       0.976       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   454/499     38.7G   0.03121   0.06743 0.0002308   0.09887       472      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.981       0.977       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   455/499     38.7G   0.03076   0.06075  0.000229   0.09174       531      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.982       0.976       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   456/499     38.7G   0.03083   0.06555 0.0002207    0.0966       908      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.983       0.975       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   457/499     38.7G   0.03072   0.06684 0.0002198   0.09778       760      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.981       0.977       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   458/499     38.7G   0.03057   0.06489 0.0002176   0.09567       748      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.981       0.977       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   459/499     38.7G   0.03096   0.06248 0.0002164   0.09365       816      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.981       0.977       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   460/499     38.7G   0.03047    0.0624 0.0002195    0.0931       709      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982       0.976       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   461/499     38.7G   0.03034   0.06626 0.0002154   0.09681      1065      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.983       0.976       0.988       0.698\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   462/499     38.7G   0.03067   0.06606 0.0002355   0.09697       769      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.983       0.975       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   463/499     38.7G   0.03037   0.06416 0.0002223   0.09475       600      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009       0.983       0.976       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   464/499     38.7G   0.03082   0.06345 0.0002051   0.09448       469      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.60it/s]\n","                 all         999       45009       0.983       0.976       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   465/499     38.7G   0.03074   0.06645 0.0002088   0.09741      1018      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.53it/s]\n","                 all         999       45009       0.983       0.976       0.988       0.698\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   466/499     38.7G   0.03063   0.06383 0.0002149   0.09467       710      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.982       0.977       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   467/499     38.7G   0.03088   0.06587 0.0002073   0.09696       584      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.64it/s]\n","                 all         999       45009       0.982       0.977       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   468/499     38.7G   0.03076   0.06427 0.0002067   0.09524       920      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.983       0.976       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   469/499     38.7G    0.0315   0.06505 0.0002397    0.0968       699      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.694\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   470/499     38.7G    0.0321   0.06778 0.0002399    0.1001       808      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.693\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   471/499     38.7G   0.03206   0.06599 0.0002264   0.09828       836      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.694\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   472/499     38.7G   0.03181   0.06837 0.0002383    0.1004       536      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.983       0.974       0.988       0.693\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   473/499     38.7G   0.03129   0.06413 0.0002357   0.09565       403      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.61it/s]\n","                 all         999       45009        0.98       0.977       0.988       0.694\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   474/499     38.7G   0.03153   0.06796 0.0002122    0.0997       815      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.57it/s]\n","                 all         999       45009       0.981       0.976       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   475/499     38.7G   0.03086   0.06567 0.0002279   0.09675      1000      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   476/499     38.7G   0.03149   0.06896 0.0002155    0.1007       702      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.53it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   477/499     38.7G   0.03177   0.06752 0.0002324   0.09953      1106      1280: 100% 46/46 [01:01<00:00,  1.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.983       0.975       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   478/499     38.7G   0.03173   0.06735 0.0002377   0.09931       865      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   479/499     38.7G   0.03145   0.06769 0.0002109   0.09935       718      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.983       0.974       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   480/499     38.7G   0.03107   0.06362 0.0002096   0.09491       797      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   481/499     38.7G   0.03076   0.06459 0.0002044   0.09556       572      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.55it/s]\n","                 all         999       45009       0.983       0.974       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   482/499     38.7G   0.03115   0.06599 0.0002265   0.09737       689      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.984       0.974       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   483/499     38.7G   0.03094   0.06586 0.0002303   0.09703       852      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.984       0.974       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   484/499     38.7G   0.03077   0.06758 0.0002196   0.09858       971      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.984       0.973       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   485/499     38.7G   0.03034   0.06329 0.0002147   0.09384       723      1280: 100% 46/46 [00:59<00:00,  1.28s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.983       0.973       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   486/499     38.7G   0.03073   0.06479 0.0002273   0.09575       564      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.56it/s]\n","                 all         999       45009       0.983       0.975       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   487/499     38.7G   0.03091   0.06703 0.0002226   0.09816       877      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.54it/s]\n","                 all         999       45009       0.984       0.974       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   488/499     38.7G   0.03072   0.06609 0.0002205   0.09703       966      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.984       0.974       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   489/499     38.7G   0.03116   0.06746 0.0002127   0.09884      1075      1280: 100% 46/46 [01:01<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.984       0.974       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   490/499     38.7G   0.03073     0.066 0.0002375   0.09697       851      1280: 100% 46/46 [01:00<00:00,  1.32s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.51it/s]\n","                 all         999       45009       0.981       0.977       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   491/499     38.7G   0.03051    0.0639 0.0002108   0.09461       778      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.63it/s]\n","                 all         999       45009       0.982       0.977       0.988       0.698\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   492/499     38.7G   0.03076   0.06507 0.0002252   0.09606       579      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.981       0.977       0.988       0.699\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   493/499     38.7G   0.03086   0.06455 0.0002209   0.09562       685      1280: 100% 46/46 [01:00<00:00,  1.33s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009        0.98       0.977       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   494/499     38.7G   0.03114   0.06549 0.0002238   0.09685       811      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.982       0.977       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   495/499     38.7G   0.03118   0.06474 0.0002367   0.09615       647      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.59it/s]\n","                 all         999       45009       0.982       0.976       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   496/499     38.7G   0.03041   0.06312 0.0002289   0.09376       449      1280: 100% 46/46 [00:59<00:00,  1.29s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.58it/s]\n","                 all         999       45009       0.982       0.976       0.988       0.695\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   497/499     38.7G   0.03121   0.06335 0.0002232   0.09478       825      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:15<00:00,  1.52it/s]\n","                 all         999       45009       0.982       0.975       0.988       0.696\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   498/499     38.7G   0.03068   0.06373 0.0002235   0.09463       907      1280: 100% 46/46 [00:59<00:00,  1.30s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:14<00:00,  1.62it/s]\n","                 all         999       45009       0.981       0.976       0.988       0.697\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","   499/499     38.7G    0.0307   0.06396 0.0002227   0.09488       749      1280: 100% 46/46 [01:00<00:00,  1.31s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 23/23 [00:35<00:00,  1.55s/it]\n","                 all         999       45009       0.986       0.972       0.988       0.697\n","                 car         999       24873       0.994       0.993       0.998       0.773\n","                 hov         999        1863       0.991       0.999       0.996       0.799\n","              person         999       12267       0.979        0.94       0.976       0.585\n","          motorcycle         999        6006       0.979       0.956       0.983       0.632\n","500 epochs completed in 10.865 hours.\n","\n","Optimizer stripped from runs/train/exp/weights/last.pt, 162.6MB\n","Optimizer stripped from runs/train/exp/weights/best.pt, 162.6MB\n","Images sizes do not match. This will causes images to be display incorrectly in the UI.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: \n","\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n","\u001b[34m\u001b[1mwandb\u001b[0m:      metrics/mAP_0.5 ▁▅▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇████▇██████████████████\n","\u001b[34m\u001b[1mwandb\u001b[0m: metrics/mAP_0.5:0.95 ▁▃▃▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇███████████\n","\u001b[34m\u001b[1mwandb\u001b[0m:    metrics/precision ▁▄▅▆▆▆▆▇▇▆▇▇▇▇▇▇▇▇▇█▇▇▇█████████████████\n","\u001b[34m\u001b[1mwandb\u001b[0m:       metrics/recall ▁▄▅▅▆▆▇▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇█▇█████████████\n","\u001b[34m\u001b[1mwandb\u001b[0m:       train/box_loss █▅▅▅▄▄▄▄▄▄▄▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁\n","\u001b[34m\u001b[1mwandb\u001b[0m:       train/cls_loss █▄▄▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n","\u001b[34m\u001b[1mwandb\u001b[0m:       train/obj_loss 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metrics/recall 0.97192\n","\u001b[34m\u001b[1mwandb\u001b[0m:       train/box_loss 0.0307\n","\u001b[34m\u001b[1mwandb\u001b[0m:       train/cls_loss 0.00022\n","\u001b[34m\u001b[1mwandb\u001b[0m:       train/obj_loss 0.06396\n","\u001b[34m\u001b[1mwandb\u001b[0m:         val/box_loss 0.07761\n","\u001b[34m\u001b[1mwandb\u001b[0m:         val/cls_loss 0.0095\n","\u001b[34m\u001b[1mwandb\u001b[0m:         val/obj_loss 0.34595\n","\u001b[34m\u001b[1mwandb\u001b[0m:                x/lr0 0.001\n","\u001b[34m\u001b[1mwandb\u001b[0m:                x/lr1 0.001\n","\u001b[34m\u001b[1mwandb\u001b[0m:                x/lr2 0.001\n","\u001b[34m\u001b[1mwandb\u001b[0m: \n","\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mexp\u001b[0m: \u001b[34m\u001b[4mhttps://wandb.ai/tunyu/YOLOR/runs/578by4qv\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 342 media file(s), 1 artifact file(s) and 0 other file(s)\n","\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20221210_152101-578by4qv/logs\u001b[0m\n"]}]},{"cell_type":"markdown","source":["F1 and Precision Recall Curve"],"metadata":{"id":"Yx8GM4UVv7zh"}},{"cell_type":"code","source":["from IPython.display import Image\n","\n","chosen_folder= '/content/yolov7/runs/train/exp/'\n","display(Image(chosen_folder + \"F1_curve.png\", width=800, height=400))\n","display(Image(chosen_folder + \"PR_curve.png\", width=800, height=400))\n","display(Image(chosen_folder + \"confusion_matrix.png\", width=720, height=540))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"4DMVP-Egv8KE","executionInfo":{"status":"ok","timestamp":1670724845491,"user_tz":-480,"elapsed":1041,"user":{"displayName":"yu 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bLFRBkAAACAA6st1xZnBsrhjLvLd1P3zJ8+XXrefvw4dlZXU/esRbUTZSIi/viPG3eP3YZ789GTby/VL1y9FCEoAwAAHAqCMmSMoAwAAADAQXZ24GzpuR4TZTrPVIY9Nu/fT92zFtVOlIloblAml8tVrF/ac/XS4OnK+kn64BIAAMBBJyhDtuT8lQYAAAA4yHYHZYrFYqp+yYkyERGFu80Ne5w6FXH0aHVn3367sXfZ7eSR7tLzgyd7BGW6ByO6j5RrQRkAAOAQkCogW6xeAgAAADjQkquXnm4+jcWNxVT9dgdlNu/dS9WvVrlcxE/8RHVnmx2UOXWkPFHmwZP1vQ8dKQeXBGUAAIDDQFCGjBGUAQAAADjIkhNlItKvX2o/ejRyPeVAyOa95q5eiqg+KHPrVsTTp429S9KJxESZueWN2Nreef7QkcTqqifpV2EBAAAcdIIyZIuJMgAAAAAHWr2DMrlcLvKnT5XqZk+Uiag+KFMsRvzgB429S1Jy9dL2TjHmVzaeP1QRlDFRBgAAyD5BGTJGUAYAAADgIDvdfzpyic9w0gZlIiI6T5fDHoWZmdT9avWJT1R/9o//uHH32O3kUE9Fvef6pWRQZvVRxOZag28FAACwvwRlyBYTZQAAAAAOtM72zjjRd6JU1yUoc/Fi6blw61YUNzdT96zFtWsR7e3VnX377cbeJSk5USYi4v29gjL9JyrrlbkG3ggAAGD/CcqQMYIyAAAAAAfd2OBY6fnO4p3U/bqvXS09Fzc3Y+P27dQ9a3p/d8Rrr1V3dj+DMvcX95gW0z9SWT+db+CNAAAA9p+gDNliogwAAADAgXd56HLp+faT27FT3EnVr/vatYp6/YfXU/V7GdWuX/rjP44oFht7lx8b6M5Hf1dHqd57oswrlfXKBw2+FQAAwP4SlNnDZz7zmbh48WJcunRpv69CzQRlAAAAAA668eHx0vPa1lrcXb6bql/n+fOR6+kp1evXmx+U+YmfqO7c0lLE9HRj75KUnCrzYK+gTN9oZW31EgAAkHGCMnuYnZ2NqampmJqa2u+rUCsTZQAAAAAOvPGh8Yp6YnEiVb9ce3t0v/pqqV6/cSNVv5dRbVAm4tlUmWY5URGU2WP1Uu+xqPjhM6uXAACAjBOUIWMEZQAAAAAOuktDlZOcJxbSBWUiIrqvXS09r7/7bhS3t1P3rEW1q5ciIt5+u3H32O3UkfKknT0nyrR3RPQdL9dWLwEAABknKEO2mCgDAAAAcOD15nvjTP+ZUn1r8Vbqnt3XrpWei6urUZiZSd2zFidPRhw//uJzEc0NyrySmCgzt7wRxWLx+UPJ9UtWLwEAABknKEPGCMoAAAAAtILx4fL6pXpMlOkar1znVJicSt2zFrlc9euX3nmnsXdJOtqbLz1v7xRjeWPr+UP9iaCM1UsAAEDGCcqQLSbKAAAAALSEy0OXS8/TS9NR2C6k6tc5NlZRF6amUvV7GdWuX7p1K2J9jy1IjTDU21lRP1ndfP5QMihj9RIAAJBxHft9gZfxC7/wCw3tPzdnvGjLEpQBAAAAaAlXhq+UnreL2zH5ZDJePfrqS/drHxqK9uHh2F5YiIj9Ccp8/OPVndvZiXj33YhPfrKx94mIGEpMlImIWFgtxNmjvZWHKoIyJsoAAADZ1pJBmbfeeityAhEAAAAA0LKSE2UiIm4u3EwVlImI6LxwIdZ+HJSZnEzV62W8/nr1Z3/4w2YFZSonyizuNVGmLxGUKSxHFFYjOnufPwcAAJABLb16qVgs7vcVOFCEpwAAAABaxdiRsehoK/8c363FW6l7dp4/X3rej4ky165Vf/aHP2zcPZKG95go85zkRJmIiKcmbgMAANnV0kGZXC4XxWKxIb9oQaYMAQAAALSMfFs+Lhy5UKonFiZS9+y8cL70vDU/H9srT1P3rEV/f8TYWHVnmxWUGeqpnCjzZG2PiTK7gzLWLwEAABnWkquXjh49Ggs/GqH6T//pP41f+IVfqFvvYrEYf+7P/bmYnp6uW0+aRVAGAAAAoJWMD42XAjL1nigT8WyqTM/HatiHVAevvx5RzUeLzQrKDHR3RFsuYudHPxv4wtVLERErHzT+YgAAAPukJYMyn/rUp+K//Jf/ErlcLmZmZmKs2h/TqFJHR0v+sWCiDAAAAEBLGR8ej5h89vzg6YNYLizHQOfAS/frOiBBmf/8n1987s6diNXViN7ext6nrS0XR3rysfCjgMzeq5deqawFZQAAgAxrydVLn/rUp0rP/+f//J99vAkHi6AMAAAAQCsZHxqvqG8v3k7VLz82VvHDVIWpqVT9XsbrVeZyisWId95p7F1+bKi3vH7pyV4TZXqPRbQlfnhQUAYAAMiwlg7KFIvFePfdd2NjY2Ofb8SBYKIMAAAAQEsZH64MytxcuJmqX1tnZ+RPny7VhcnJVP1eRrVBmYiI//W/GnePpKHefOl5z4kybW2VU2WWHzThVgAAAPujJYMyn/70pyMiIpfLxfb2drz99tv7fCMOBkEZAAAAgFZysu9k9OX7SvXEwkTqnp0XLpSe92uiTLWb3ZsWlOkpB2UW1/aYKBMRMXCi/Lz8foNvBAAAsH9aMihz6dKlOHLkSBSLxYiI+L//9//u8404EEyUAQAAAGgpuVwuLg9dLtW3Fm+l7tl5/nzpuTA1VfoMsVl6eiI+8YnqzjYrKDOcWL20uNfqpYiIgZPlZxNlAACADKvyZxsOnr/0l/5SfP/734+IiIWFhbr2/u3f/u1YW1ura0+aQVAGAAAAoNVcHrocb88/mxg9sTgRxWIxcil+IKrzwvnS887Tp7E1Px/50dGUt6zNG29EVPOzfTdvRiwsRAwPN/Y+RxKrlxb3Wr0UsWv10geNvRAAAMA+atmgzDe/+c2G9X7jjTca1psGMlEGAAAAoOWMD4+Xnp9sPImHaw9jpHfkpft1JSbKRDybKtPsoMyf+lMRX/96dWf/9/+O+NznGnufoZ7yRJkna5uxs1OMtrZdn6UlJ8o8nY/Y3oxozwcAAEDWtOTqJdiboAwAAABAq7kyfKWinliYSNWvc3dQZnIqVb+XUcvP4X3ve427x48N95UDLzvFiOX1recPDZxIFMWIlbnGXwwAAGAfCMqQHSbKAAAAALScy0OXK+qJxXRBmY4TJyLX3V2qC1NTqfq9jFdfjRgYqO7s5GRj7xIRcaSncjLM4toe65eSE2UiIlbeb+CNAAAA9o+gDBkiKAMAAADQaoa7h+N4z/FSnXaiTK6tLTrHxkr1fgRl2tsjPvGJ6s7Ozjb2LhERw72dFfXC6ubzhwZeqayXBWUAAIBsEpQhO0yUAQAAAGhJ40PjpeebCzdT9+u8cKH0vHH7dup+L+Ps2erONSMoM9RbOVFmYbWKiTLLDxp4IwAAgP3TkkGZz3zmM/GZz3wm/vpf/+v7fRUOFEEZAAAAgFb02tHXSs8TixOxub3HxJMadF0pB282Z2Zie3k5Vb+XcZCCMkf7KifKPF7ZIyjTczSiLRGoMVEGAADIqI79vsDL+O53vxu5XC4uXbr0wrNvvvlmRESMjIzEP/7H/7jRV2M/yckAAAAAtKRkUGZrZytuP7ld8Xu16r52raJev3Ej+t5446X7vYxqgzLLyxFPnkQcOdK4uxzv76qo55Y3nj/U1hbR/0rE0t0fXUxQBgAAyKaWnChTi9/8zd+Mr371q/HNb35zv69Cw0nKAAAAALSi3aGYdx+/m6rf7qDMxo0bqfq9jDNnqj/b6Kky3fn2ONJTnhYzt7y+98GBE+VnQRkAACCjMh+U4RDJCcoAAAAAtKKxwbHobu8u1WmDMvnR0WgfOV6q169fT9XvZVQ7USYi4u7dxt3jx0YHylNl9pwoExHRV/4zi7XHDb4RAADA/hCUIUMEZQAAAABaUXtbe1wZvlKq0wZlIiqnyhz0oEyjJ8pERIwOloMy80sfEpTpGS4/rwrKAAAA2SQoQ3aYKAMAAADQspLrl957/F7sFHdS9eu+erX0vHH7TuysraXqV6uRkYjOzurONiUoM1Ce2POhq5d6jpaf1xYafCMAAID9IShDhgjKAAAAALSqV4++Wnpe2VyJudW5VP2SE2ViZyc23nsvVb9atbVFnD5d3dnmBGWqWL3Um5gos/4kYme7wbcCAABoPkEZssNEGQAAAICWNTY4VlHPLM2k6td97fWKeu0Ar19qRlBmJBGUWS1sx8rG1vOHkquXohixttj4iwEAADSZoAwZIigDAAAA0Kp2B2Wml6dT9cufPhVtR46U6vUDHJSZSZcJqkoyKBMRMbe0x/ql5OqlCOuXAACATBKUITtMlAEAAABoWaO9o9HVXg5zzC6lG7OSy+Wi+9rVUn2QgzKTkxGbm429y+hAd0W95/ql3t1BmccNvBEAAMD+EJQhQwRlAAAAAFpVW64tzg6UkyXTS+kmykREdF/jiv8JAAAgAElEQVS7VnremLgVxUIhdc9aXLxY3bmtrYg7dxp7l9HBXRNl9grK7J4osyooAwAAZI+gDNmR89cZAAAAoJWdGzhXep5ZTr+PqPtqOSgTm5uxcetW6p61eO216s/euNG4e0REjO5avTS/Z1BmuLI2UQYAAMigjv2+QBqPHz+Of/7P/3ndzyb96q/+as3fwz6xegkAAACgpY0NjpWeZ5dnY6e4E20pfjgqOVEm4tn6pd2/10hXr774zI+9+27j7hER0d/VET359ljb3I6IiLnl9ecPPbd6aaGxlwIAANgHLR2UWVhYiC9/+csvPFcsFqs+u5ugTCsRlAEAAABoZecGyxNlNrY34oOnH8TJ/pMv3a/z/FjkenqiuLb2rOet26nvWIvjxyOOHYt49OjFZxsdlMnlcjE62BXTj1YjImJ+aY+JMvneiPauiO0ffc3qJQAAIIMyv6sml8tF7iUmjRSLxQbchoYyUQYAAACgpSUnykRETC9Pp+qXa2uL/Mly0GZr7oNU/V5GteuXGh2UiahcvzS31+qlXK5y/ZLVSwAAQAa1bFCmWCw29BetSFAGAAAAoJWdGzhXUc8szaTu2fHKaOl584O51P1qVW1Q5saNiEZ/LDk60F163nP1UkTl+iWrlwAAgAxqydVL3/nOd/b7ChxEcjIAAAAALW2kdyTybfnY3NmMiIgHTx+k7pkffaX0vPVB8yfKXL1a3bmlpYj33484+fKbpl5o5EUTZSIiehJBGauXAACADGrJoMxP//RP7/cVOJAkZQAAAABaWVuuLU70nYjZ5dmIqE9QpuOVRFBmbi6KxeJLrWp/WdVOlImIeO+9xgZlRgfLQZnF1c3Y2NqOro72ykO9Vi8BAADZ1rKrl+A5TfyAAwAAAIDGONV3qvT8YKUeQZny6qVioRDbi4upe9bi1VerPzs52bh7RFSuXoqImN9rqkxPIiizavUSAACQPYIyZIigDAAAAECrO9lfHqly/+n91P3yiYkyEc1fvzQ2FtHe/uJzEc0IynRV1HuuX0quXloTlAEAALJHUIbsMFEGAAAAoOUlJ8rMrc7F5s5mqn4d+xyUyecjzp2r7uydO429y8juoMzSHkGZ3kRQZvNpxOZ6Yy8FAADQZIIyZIigDAAAAECrS06U2SnuxNzqXKp+HaOVQZnNJgdlIiIuXqzuXKODMrsnyswv7xGC6a/884rl9FN9AAAADhJBGbLDRBkAAACAlpecKBMRcX8lXVCj4/ixit1HWx+kC968jAsXqjvX6KDMcG9ndLSVP0Ob32v10tCu8TcL0429FAAAQJMJypAhgjIAAAAArS45USYi4sHTB6n65drbo+P48VK9NXdwJ8p88EHE06eNu0dbW65i/dLcnkGZscp6caZxFwIAANgHgjJkh4kyAAAAAC3vRO+JyCV+ICrtRJmIiI5XyuuEDvLqpYiIycnG3SOicv3SnkGZgRMRbflyvWiiDAAAkC2CMmSIoAwAAABAq8u352Okd6RUp50oExGRTwRl9mP1Ui1BmUavXxoZ6C49zy2vP3+grT1i6Gy5tnoJAADIGEEZssNEGQAAAIBMONV3qvR8b/le6n75U+V1Tpuzs1EsFlP3rMWBmigzmJgos7THRJmIiKFz5WerlwAAgIwRlCFDBGUAAAAAsuDcYDmoMbmUPjmSP1fut7O6GtsPH6buWYujRyMGB6s72+iJMsnVSw9XNmJ7Z4/Q0NBY+dnqJQAAIGMEZcgOE2UAAAAAMuHikfIIlrnVuVgprKTq1zl2vqIuTDc3/JHLRVy4UN3Ze+kH6Hyk0cTqpZ1ixKOne0yVGU4EZVY+iNhca+ylAAAAmkhQhgwRlAEAAADIggtHKlMlU0tTqfp1nh+rqJsdlImIOHOmunP37zf2HsmJMhEfsn5pqPLPKxZnG3gjAACA5hKUITtMlAEAAADIhN1Bmckn6dYv5U+ejFw+X6oLU80Pypw+Xd25hk+UGawMyswvVxOUsX4JAADIDkEZMkRQBgAAACALzgyciY62jlJ958mdVP1y7e2RP3euVO/HRJlTp6o79+BBxM5O4+4xsnuizPL684eGzlXWT0yUAQAAskNQhuyQkwEAAADIhHxbPs4NlMMaaSfKRER0jpWnpBSmplL3q1W1E2U2NyMePmzcPY73d1UMZt5z9VLvscp69VHjLgQAANBkgjJkiKQMAAAAQFYk1y+lnSgTsSsoMzMTxUaObdlDtUGZiMauX8q3t8XR3s5SPbfX6qX2jojuI+V6daFxFwIAAGgyQRmyIycoAwAAAJAVF49cLD3PLs3G5s5mqn7JoExxfT225uZS9atVtauXIiLu32/cPSIq1y/N7xWUiYjoOVp+Xnvc2AsBAAA0kaAMGSIoAwAAAJAVF4fKQZmt4lbcWUw3Vabz/FhFXZiaTtWvVgdlokxExOhgd+l5bnl970O9yaCMiTIAAEB2CMqQHSbKAAAAAGTG1aNXK+rrj66n6pecKBMRUZhublDm2LGIzs4Xn4to/ESZ0cREmT1XL0VUTpRZNVEGAADIDkEZMkRQBgAAACArzg+ej56OnlJ94/GNVP06Xnklct3lSSqFqalU/WqVy1W/fqnhE2V2BWWKxeLzh3qtXgIAALJJUIbsMFEGAAAAIDPa29rj1eFXS/WNR+mCMrm2tug8d65UN3uiTET165eaGZQpbO3E0trW84dMlAEAADJKUIYMEZQBAAAAyJKrx8rrl95beC+2d7ZT9UuuX9qPoEy1E2UavnppsLuinltef/5QcqLM+mLEzk5jLwUAANAkgjJkh4kyAAAAAJly7di10vPa1lpMLU2l6td5vhyU2ZyZieJ2uuBNraqdKDM5GbHXNqR6SU6UiXi2fuk5PcPl5+LOs7AMAABABgjKkCGCMgAAAABZcvXo1Yr6+qPrqfolJ8oUNzdj88H7qfrV6syZ6s4tLUVMTTXuHqMDNU6UiYhYW2jchQAAAJpIUIbsMFEGAAAAIFMuHrlYUT94+iBVv2RQJiKiMD2Vql+tXn+9+rPf/37j7jGye6LM0l4TZXYFZVYfN+5CAAAATSQoQ4YIygAAAABkSb49H0e7y4GNudW5VP06z5+vqAuNHNuyh09+svqzjQzK9HS2x0BXR6nec/XScxNlBGUAAIBsEJQhO0yUAQAAAMic0d7R0vP86nyqXu3Hj0dbb2+pLkxPp+pXqxMnIl55pbqzjQzKRESMDJanyuwZlDFRBgAAyChBGTJEUAYAAAAga0Z6RkrP82vpgjK5XC7y58vrl5odlImofqrM//t/jb3HaGL90tzS+vMHTJQBAAAySlCG7DBRBgAAACBzkhNl0q5eiojoHCsHZTanmh+U+cmfrO7c7GzEo0eNu8foQHfpeX5lj4ky+d6I9s5ybaIMAACQEYIyZIigDAAAAEDWjPSWJ8o8XHsYO8WdVP2SQZnCvXtR3NpK1a9W1U6UiYh4553G3SM5UWZ+aY+gTC5XuX7JRBkAACAjBGXIDhNlAAAAADInOVFmu7gdj9fTBTY6x86Xi62t2Lx3L1W/WtUSlJmYaNw9RgfLQZnlja1YK2w/fyi5fslEGQAAICMEZQAAAACAA2u0Z7Sinl+dT9UvOVEmIqIwNZWqX60uXYrI56s729CgTGL1UkTE3PL684dMlAEAADJIUIbsMFEGAAAAIHOSq5ciIubXUgZlLpyvqAvT06n61aqjI+LixerONjYo01VRzy3vsX6pd7j8vLrQuMsAAAA0kaAMGSIoAwAAAJA1ydVLERFzq3Op+rUPDUXb4GCpLkw1NygTEXHlSnXnbt5s3B2Sq5ciIuaW9gjKmCgDAABkkKAM2ZHz1xkAAAAga4a7hqM9116q065eyuVyFeuXmj1RJiJifLy6c7dvR+zsNOYOI9WsXupNBGVWBWUAAIBskCwgO6xeAgAAAMic9rb2ONZzrFTPraWbKBMRlUGZycnU/WpVbVBmYyNidrYxdxjs7oiujvLHw3uuXkpOlNlai9hca8xlAAAAmkhQhgwRlAEAAADIotGe8vqltKuXIiK6EkmVzfv3Y2thIXXPWlQblIlo3PqlXC4XIwPl9Ut7rl5KTpSJMFUGAADIBEEZssNEGQAAAIBMOtl/svQ8szSTul/Pxz9WUa//4Aepe9biypXqz05MNO4eo8mgzF6rl3p2BWXWBGUAAIDWJyhDhgjKAAAAAGTRpaFLpefppelY20q3Aqj7Y5VBmbV33knVr1anT0d0d1d39r33GneP0YHyJeb3Wr1kogwAAJBBgjJkh4kyAAAAAJk0PlTeVVSMYtxZvJOqX/vgYHSOjZXq9XeaO1GmrS3i8uXqzl6/3rh7jA6WJ8rsGZQxUQYAAMggQRkyRFAGAAAAIIuuDFfuKrq5cDN1z+6Pf7z0vPbOO1EsFlP3rMVrr1V3rpFboZKrlx49LcTm9k7lARNlAACADBKUITtMlAEAAADIpLMDZ6O7vbwmaGJxInXPno+X1y9tP3wYW++/n7pnLXZtf/pQ778f8fBhY+6QXL0UEfFwZddUme6hynptoTEXAQAAaCJBGTJEUAYAAAAgi9rb2uPi0MVSPbGQPiiTnCgTEbF+40bqnrWoNigT0bipMiOJ1UsREXNLu4Iy7R0R3UfKtaAMAACQAYIyZIeJMgAAAACZNT40Xnqux+qlriuvVtTr776bumctduV0PlKjgjLJ1UsREXPLG88f6kmsX7J6CQAAyABBGTJEUAYAAAAgq8aHy0GZx+uP4/F6utBGe39f5M+dK9UbN5oblLl0KaKr68XnIhoZlKlcvTS3vP78od5EUGZNUAYAAGh9gjJkh4kyAAAAAJl1eehyRT35ZDJ1z+5Xy1Nl1t97L3W/WrS3R1y7Vt3ZRgVljvV1Rntb+TO151YvRZgoAwAAZI6gDAAAAABw4J0/cr6innoylbpn12vloMzmzExsrzxN3bMWH/tYded++MPGvL+tLRfH+ztL9Z6rl0yUAQAAMkZQhuwwUQYAAAAgs072nYyu9vKuorpMlHnttYp64+bN1D1rUW1QZnEx4nGDMirJ9Uvze61eMlEGAADIGEEZMkRQBgAAACCr2nJtMTY4VqqnlqZS9+x6dVdQ5r13U/esRWLz0wvdvt2YO4wMlMNHL5wos74YsbPTmIsAAAA0iaAM2WGiDAAAAECmXThyofRcj4ky+dOnom1goFSvv/de6p61uHSp+rO3bjXmDqPJoMzSHkGZnuHyc3HnWVgGAACghQnKkCGCMgAAAABZdn7wfOn53sq9KGwXUvXL5XLRlUirFCanUvWr1cWL1Z9t1ESZZFDm4cpG7OwUKw8kJ8pERKwtNOYiAAAATSIoQ3aYKAMAAACQacmJMtvF7Zhdnk3dszORVincuZO6Xy16eyNOnarubKMmyowMdpeet3aKsbC6K3zUsysos/q4MRcBAABoEkEZMkRQBgAAACDLzh85X1FPPZlK3bPrYjl8szU/H9vLy6l71uLy5erONWP1UkTE3PKu9UvJ1UsREWuCMgAAQGsTlCE7TJQBAAAAyLQLgxcq6onFidQ9O3ftP2r2VJnE5qeP1IzVSxF7BGV2r14yUQYAAGhxgjJkiKAMAAAAQJb15nvj7MDZUv3u43dT9+y8UBm+2bgzmbpnLaqdKPP++xErK/V//2hi9VJExNzSeuWB3auXTJQBAABanKAM2WGiDAAAAEDmvXb0tdJzXYIyZ89G5POlutkTZaoNykRENOJqI/0vmCjT2RfR3lmuTZQBAABanKAMGSIoAwAAAJB1V49eLT3fW7kXTzaepOqX6+iIzrFzpXpj8mCuXoqIuHWr/u/v7GiL4d5yUGh+d1Aml6ucKmOiDAAA0OIEZcgOE2UAAAAAMi85USaiPlNlui5cLD0Xmrx6qZagzO3bjbnD6EB5/dLc8vrzB3oTQRkTZQAAgBYnKEOGCMoAAAAAZN3VY1cr6rqsX7qYCMrMzERxczN1z2oNDUUcO1bd2UZMlImIGB0sr1+aW9p4/oCJMgAAQIYIypAdJsoAAAAAZN7xnuMx0jNSqm88vpG6Z9fFC+ViaysKs3dT96zF5cvVnWtUUGakPxGU2b16KSKid7j8vLrQmEsAAAA0iaAMGSIoAwAAAHAYJNcvvff4vdT9khNlIiIKk3dS96xFteuXGrV6aSQ5UWZ5PYrFYuUBE2UAAIAMEZQhO0yUAQAAADgUxofHS89TS1OxuZNuVVLnhQsV9cad5gZlqp0oMzMTsbHHwJe0Rge6S8/rmzuxvLFVeaA3EZRZFZQBAABam6AMGSIoAwAAAHAYXB4qJ0u2drZiZmkmVb/2/v7oGB0t1YU7k6n61araiTLFYsTUVP3fPzrQVVHPLe1K4yQnymytRWyu1f8SAAAATSIoQ3bIyQAAAAAcCsmgTETErcVbqXsm1y8VDuhEmYiIW+n/U5+zOygzv7wrKJOcKBNhqgwAANDSBGXIEEkZAAAAgMPgwpEL0ZYrf7RZj6BM18Xy+qWNyckoFoupe1ar2okyERG3b9f//aOD3RX13PJ65YGeXUGZNUEZAACgdQnKkB05QRkAAACAw6C7ozvODpwt1bcX06dHOi+W0yo7S0ux/ehR6p7VGh2N6O+v7uzERAPeb6IMAABwiAjKkCGCMgAAAACHxaUj5WDLxEL69EhyokxExEYT1y/lctVPlXnnnfq/v6+rI/o620v13O6gzHMTZRbqfwkAAIAmEZQhO0yUAQAAADg0Lg9fLj3PLs9GYbuQql/+9OmKeuuDuVT9avXqq9Wde/vtiEZshUquX5pb2rV6afdEGauXAACAFiYoQ4YIygAAAAAcFpeHykGZ7eJ2zCzNpOrXMTJSUW/Nz6fqV6tPfKK6c4uLEbOz9X//SGL90nMTZbqHKmsTZQAAgBYmKEN2mCgDAAAAcGicGzhXUd9buZeqX1tvb7T195fqrbnmTpT5iZ+o/uzbb9f//aMfFZRp74joHCjX60/qfwEAAIAmEZQhQwRlAAAAAA6LMwNnKurZ5fRjVjpGR0vPhy8o8xGrlyIiuo+Un9cW638BAACAJhGUITtMlAEAAAA4NAY7B2MgX55ycnflbuqe+xmUOXMmYni4urONCMokVy8trW/F+uZ25YGexPqldUEZAACgdQnKkCGCMgAAAACHRS6Xq5gqc3e5DkGZkZHS89b8fOp+tcjlqp8q0+jVSxER87vXL3UngzJWLwEAAK1LUIbsMFEGAAAA4FCpe1BmtByU2Zyfj2KxmLpnLaoNyty+HbG+x3akNEYHK4Myc8u7XmD1EgAAkBGCMmSIoAwAAADAYXKmPxGUWbmbOtiST6xeKq6uxs7Tp6n61araoMzOTsTNm/V99+hAd0X93EQZq5cAAICMEJQhO3L+OgMAAAAcJsmJMhvbG/Fw7WGqfsnVSxERW3PNXb/0+uvVn71+vb7v3r16ac7qJQAAIKMkC8gOq5cAAAAADpVkUCYiYnZ5NlW/jsREmYiIrbm5VP1q9dpr1Z+9caO+7x7qzUdne/nj4rml3UGZxOql9SfPxtoAAAC0IEEZMkRQBgAAAOAwOdt/tqK+u3I3Vb/ngjLzzQ3KDA5GnDnz4nMR9Q/K5HK5GElMlZlbXq88kFy9VNyJKCzX9wIAAABNIihDdpgoAwAAAHConOg/EW2Jddx3l1MGZfZ59VJExNWr1Z2r9+qliNgVlPmI1UsR1i8BAAAtS1AGAAAAAGhJ+bZ8nOw7WarTBmXaenqibWCgVG/NfZCq38uoNihz82bE1lZ9332sr7P0vLC6WfnF5OqliIi1xfq+HAAAoEkEZcgOE2UAAAAADp0z/eVdRWlXL0VEdLxSXr+0+X7zgzLXrlV3bnMz4s6d+r57qDcRlHlaqPxiz+6JMoIyAABAaxKUIUMEZQAAAAAOmzMDiaBMyokyERH5k6dKz5v376fuV6tqJ8pERLz7bn3fPdybLz0vrO4Kyli9BAAAZISgDNlhogwAAADAoZMMysyvzcfa1lqqfvlTrROUmZmp77uHE6uXlte3Ymt7p/zF3RNlrF4CAABalKAMGSIoAwAAAHDYJIMyERH3lu+l6pcMymw/ehQ76+up+tXq+PGI/v7qzk5P1/fdw4nVSxERi2ub5aL7SOVhq5cAAIAWJShDdpgoAwAAAHDonO0/W1HfXUm3fikZlImI2Lz/IFW/WuVyEWNj1Z2tf1AmX1EvPE2sX8r3RLR3lWurlwAAgBYlKEOGCMoAAAAAHDa7J8rcXU4ZlDm9OyjT/PVL585Vd66Rq5ciIhZWNysPJNcvWb0EAAC0KEEZssNEGQAAAIBDZ7BzMAbyA6W67hNlHjQ/KLN/E2V2B2UKlQeS65esXgIAAFqUoAwZIigDAAAAcNjkcrmKqTJpJ8p0jIxEdHSU6oM8Ueb99yM2Nur33t2rlxafC8okJspYvQQAALQoQRmyw0QZAAAAgEMpGZSZXZ5N1SvX3h75EydK9dY+BGWqnSgTETGb7j+3wtCuiTKPn1q9BAAAZI+gDBkiKAMAAABwGO2eKLNT3EnVL7l+afPewZ0oExExM1O/93Z2tEV/V3mazvMTZaxeAgAAWl/Hi4/QbJOTk/G9730v7t+/H4VCIU6dOhWvvfZafPrTn97vqx1sJsoAAAAAHEpnB86Wngs7hZhbnYsTfSc+4js+WkVQ5oBPlJmeru+7h3rzsbKxFRERC1YvAQAAGSQoc4D84R/+Yfzqr/5q/I//8T/2/PqlS5fiS1/6Uvzdv/t3I7dPoZDf/M3fjF/6pV+q+L2f+7mfi7feemtf7lNJUAYAAADgMDrTf6aivrt8N11Q5ky53+aDB7Gzuhptvb0v3a9WJ09GtLdHbG+/+Gw9J8pERBzt64y7C2sREbGwavUSAACQPVYvHRBvvvlm/IW/8Bc+NCQTEXH79u34+3//78dnP/vZePKk+T+xcfv27fiVX/mVpr+3aibKAAAAABxKyYkyERGzy7Op+nW9eqVcFIuxcfNmqn616uiIOHPmxeciIqam6vvuod7O0vPC049YvbS9EbG5Vt+XAwAANIGJMgfAP/kn/yT+1b/6V6U6n8/HX/krfyXeeOON6OrqiuvXr8dv//Zvx8LCQkREfOc734nPf/7z8fu///uRz+ebcsdisRh/82/+zVhdXW3K+16OoAwAAADAYXSi70R05Dpiq/hsZVDaoEz31asV9fq770bPJz+Zqmetzp2rbq3SnTv1fe9wb/nzxo9cvRTxbP1Svqe+FwAAAGgwE2X22Xe/+934F//iX5Tq8+fPx/e///341re+FV/60pfii1/8Ynzta1+LycnJ+OxnP1s690d/9Efxla98pWn3/PrXvx7f/e53IyLi2rVrTXtvTUyUAQAAADiUOto64lT/qVJ9d/luqn7506ejbWCgVK9fv5Gq38u4cKG6c7dv1/e9w4mJMosftXopwvolAACgJQnK7KNisRj/8B/+w1Ld2dkZv/u7v7tnEOXIkSPx7W9/Oy5dulT6vV//9V+Pubm5ht9zZmYmfvmXfzkiInp7e+NrX/taw9/5cgRlAAAAAA6rMwPlXUVpJ8rkcrnofu21Ur3+7rup+r2My5erO3fvXsRaHTcgVQRl1jZjZ6dY/mJy9VJExLqgDAAA0HoEZfbRf/2v/zW+973vleovfOEL8fGPf/xDz/f19cWv/dqvleqVlZX4xje+0dA7RkT8rb/1t2J5eTkiIr785S/HxYsXG/7OlyInAwAAAHBonR04W3qeXUkXlImI6LpaDspsvPdeFLe2UvesReLn5V6onuuXjvaVVy9t7xTjyVpiqsxeq5cAAABajKDMPvoP/+E/VNR/+2//7Rd+z1/8i38xTpw48aE96u2b3/xm/MEf/EFERHzqU5+KX/qlX2ro+9KRlAEAAAA4rJJBmScbT2KpsJSqX/fV8tTn4sZGFKamUvWrVbUTZSIibt2q33tHBror6rnljXJh9RIAAJABgjL76Pd+7/dKz2fPnt1z5dJuHR0d8ef//J8v1W+//XbMzqb/CZm9PHjwIN58883Se//Nv/k30d7e3pB31UVOUAYAAADgsEoGZSIipp5MperX/dqrFfXGxESqfrWqZaLM7dv1e+8rg10V9QdL6+XiuYkygjIAAEDrEZTZJ0tLSzE5OVmqf+qnfqrq79199u23367bvZK++MUvxuLis3/svvnmm/GTP/mTDXlP/QjKAAAAABxWl4cqR7BMLKQLtnSeP19RF6anU/Wr1dGjEUNDLz4XUd+JMq8MVk6UeT8ZlOnsj8glPlI2UQYAAGhBgjL75Pr16xX1+Ph41d+7++zuXvXw7/7dv4tvf/vbERFx6dKl+Gf/7J/V/R11Z6IMAAAAwKF1ZuBM9HT0lOqbCzdT9Wvr6YmOkydLdWFyKlW/WuVy1a9fqudEmZGBroqP2eaSQZm2tojuI+V6/Un9XgwAANAkgjL75M6dOxX12NhY1d+7++zuXmnNz8/HL/7iL5bqb3zjG9HT0/MR33FQCMoAAAAAHFZtubaKqTITi+lXJXWeL38OV5iaSt2vVtWuX6rnRJl8e1sc6yuvX/pgaaPyQHL9ktVLAABACxKU2SdLS0sV9dGjR6v+3uHh4Y/sldbf+3t/Lx4+fBgREX/jb/yN+OxnP1vX/g1jogwAAADAoXZl+Erp+ebCzSgWi6n6Jdcv7UdQptqJMtPTERsbLz5XrVcGy0GZitVLEZUTZaxeAgAAWlDHfl/gsFpZWamoa5nY0tvbW1EvLy/X5U4REb/zO78T//7f//uIiBgdHY1f//Vfr1vvavzGb/xG/MZv/EZVZx88eLDrdwRlAAAAAA6z8eHyyvInG09ifm0+RntHX7pfZ2Ky8/aTJ7G1sBAdu36IrZGqDcpsb0dMTER87GP1ee8rg93xw/vPfjhvbndQpic5UcbqJQAAoPUIyuyTtbW1irqzs7Pq7+3q6qqod/d6WQsLC57jVrwAACAASURBVPHFL36xVP/Wb/1WTZNu6mFpaSnu3bv3ct9sogwAAADAoZacKBMRMbEwkS4ok5goExGxOT3d1KDMq69Wf/b69XoGZaxeAgAAsktQZp/sniBTKBSq/t6NXXNUa5lG81G+9KUvxfvvvx8RET/zMz8Tf/Wv/tW69K3F4OBgnD59uqqzDx48iJ2dncTvCMoAAAAAHGbjQ+MV9c2Fm/FTp3/qpft17QrKbExNRc8nP/nS/Wp19Wr1Z2/cqN97XxnsLj3Pr2zE9k4x2tt+9Nmb1UsAAECLE5TZJ/39/RV1LVNhdp8dGBhIfZ/f//3fj3/7b/9tqd+//tf/OnXPl/Hmm2/Gm2++WdXZM2fOVE6fMVEGAAAA4FAb6h6KY93H4tH6o4iImF6aTtUvf/p0REdHxNZWREQUpqbSXrEmQ0MRp05F3L//4rPXr9fvvcmgzPZOMR6tbMToj3/P6iUAAKDFte33BQ6rwcHBinphYaHq7338+HFFnTYos7S0FF/4whdK9Ve+8pU4e/Zsqp77Q1AGAAAA4LA7N3iu9DyzPJOqVy6fj84zZ0p14c5kqn4vo9qpMvWdKFO5+r1i/VJy9VJhOWJ7q34vBgAAaAJBmX1y8eLFinp6uvqfbtl99tKlS6nu8uUvfzlmZ2cjIuJP/+k/HX/n7/ydVP32jYkyAAAAAIfeuYFyUCbtRJmIiM7EZ28bt26l7lera9eqO/fee6XBN6mNDnRX1B8srZeL5OqlCFNlAACAlmP10j65tutfuBMTE1V/761d/yDf3atWt2/frrjHlStXPvL81q5/cf/H//gf47/9t/9Wqj//+c/Hr/3ar6W608sRlAEAAAA47MYGx0rPc6tzsba1Fj0dPS/dr2v8cqz80R9FRERhejp2CoVo6+xMfc9qVfvRX6EQcedOxAs+2qtKcvVSRMT7yaBM79HKw2uPI/qOpX8pAABAkwjK7JPBwcG4cOFCTE4+G9f63//7f6/6e5OhlIiIT3ziE3W718OHD+Phw4c1fc/y8nIsLy+X6g8++KBu96mJiTIAAAAAh15y9VJExOzybFwZfvn0SNfl8XKxvR2FyanofrUOaZQqVbt6KeLZ+qV6BGWO9nVGWy5ip/isfriSWL3UN1J5+Ol8xPHxAAAAaBVWL+2jn/mZnyk9z8zMxPXr11/4PVtbW/FHP/oJlohnIZlz5859xHccJoIyAAAAAIddcvVSRMTM0kyqfl3jlyvqjVvVT4auh1qGSVfx8WJV2ttycbSvq1R/ZFBmZa4+LwUAAGgSQZl99PnPf76i/vrXv/7C7/n2t78dDx48+NAeL+N3fud3olgsVv3rx1Nwfuznfu7nKr7+1ltvpb7TSzFRBgAAAODQ2z1RZnppOlW/zgsXItrbS/VGDSvU62FkJOL48erO1isoExFxvL+8XurhcqH8hb0mygAAALQQQZl99Gf+zJ+JT3/606X6G9/4RvzgBz/40PNPnz6NX/7lXy7VfX198YUvfOFDz//8z/985HK50q99C7AAAAAAQJP05fvieE85WTK7PJuqX1tnZ3SOjZXqjVu3UvV7GdWuX7pxo37vHBn4kIky3UMRbR3lWlAGAABoMYIy+yiXy8W//Jf/slQXCoX42Z/92bixx79onzx5En/5L//luH37dun3/sE/+AfxyiuvNOWuLcFEGQAAAACicv1S2okyERFdl8vrlwoTzQ/KVLt+6caNiJ2d+rxzpP9DgjJtbZVTZQRlAACAFiMos88+85nPxD/6R/+oVE9NTcUnP/nJ+Gt/7a/FV7/61fj6178ev/iLvxgXLlyIP/zDPyyd+7N/9s/Gr/zKr+zHlQ+unL/OAAAAAESMDZYnwEw+mfyIk9WpCMrMzMTO+nrqnrWoNiizuhoxM1Ofdx5PTJSZX96o/GJfYhfUylx9XggAANAkHS8+QqN95StfiadPn8bXvva1iHg2WeZb3/pWfOtb39rz/E//9E/Hf/pP/yk6Ozv3/PrhZaIMAAAAABGXhi6Vnh+tP4rH64/jaPfRl+7XdWW8XBSLUbhzJ7qrTa/UQbWrlyKeTZU5fz79O4/3lz97fFrYjrXCdvR0tj/7jb7R8sGnD9O/DAAAoImM4DgAcrlc/NZv/Vb8wR/8Qbzxxhsfeu7ixYvx1a9+Nb7zne/E0NBQE2/YIo6Pv/gMAAAAAJk3PlT5OdHtxdsfcrI6yYkyEREbt5q7fqmWTM716/V55/HE6qWIXeuXKlYvmSgDAAC0FhNlDpDPfe5z8bnPfS7u3LkT3/ve9+L+/ftRKBTi5MmTcfXq1fgTf+JP1NTvrbfeirfeeqvu9zx//nwUi8W6931p+e6In/1qxPn/b79vAgAAAMABcHm4MtgysTARf/LEn3zpfp1jYxH5fMTmZkREbEw0Nyhz6lTE4GDE0tKLzzYqKDO/shFnj/Y+K5Krl0yUAQAAWoygzAF08eLFuHjx4n5fo3X0HI349M/v9y0AAAAAOCBGekZisHMwlgrPkiW3FtMFW3L5fHSdPx8bExMREaX/bZZc7tn6pf/5P198tl5XGxnYNVFmOTFRpj+xemljKWJz/dkPswEAALQAq5cAAAAAgEzJ5XIxPlxev5Q2KBMR0TVenlLT7NVLERGvvVbdudvptkyVPL96qVAukquXIiKeztfnpQAAAE0gKAMAAAAAZM7loXKw5dbCrdSrxDsvl/tt3r0bO6urqfrV6vLlF5+JiLh/P6IeVzva1xltuXI9n5wo0zdaeVhQBgAAaCGCMgAAAABA5owPlSfKLG8uxwerH6Tq17UrqbJRr9EtVao2KBMRcedO+ve1t+XiaF9nqX64kgzKHK88LCgDAAC0EEEZAAAAACBzLg5drKgnn0ym6tc1Pl5Rb0w0d/3SpUvVn63XZqjk+qWKoEy/iTIAAEDrEpQBAAAAADJnbHCsop5dnk3Vr/Pcuch1liesbNQrjVKlWibK1GvYzYcGZXpNlAEAAFqXoAwAAAAAkDkjPSPR09FTqqeXplP1y7W3R+fF8pSajYmJVP1qNTz87Fc16pXhOdZfDgY9eloof6GjM6Kzv1yvLdbnhQAAAE0gKAMAAAAAZE4ul4uzA2dL9czSTOqeyfVLzZ4oE1H9VJm6BWX6yhNlHq0UKr/Yk0jtrC3U54UAAABNICgDAAAAAGRScv3SzHIdgjKJpMrWgwexvbKSumctLl2q7ly9Vi8lJ8o8WduMze2d8hd7hsrPgjIAAEALEZQBAAAAADLp3MC50vPs8mxs72yn6tc1XjnSpdDkqTLVTpSZno4oFF587kWOJ4IyERELyfVLJsoAAAAtSlAGAAAAAMikc4PloMzmzma8v/p+qn7J1UsREesTE6n61araiTI7O8/CMmkdTaxeioh4uPJhQZnF9C8DAABoEkEZAAAAACCTkhNlIiJmltKtX8qfPh25np5SfVAnykRE1ONqx3ZNlHn0dKNcmCgDAAC0KEEZAAAAACCTxgbHKuq0QZlcW1t0XbxYqjcO6ESZiIjbt9O/7/iuiTKPk6uXuofKz+smygAAAK1DUAYAAAAAyKTjPcejp6M8AWZ6Of0+oq4rV0rP6zebG5Q5cSKit7e6s/WYKHN010SZD129VFiJ2CoEAABAKxCUAQAAAAAyKZfLVaxfml2aTd2za3y89Lz98GFsPX6cume1crnqp8rUY6JMX2d7dHWUP0J+tPIhq5ciTJUBAABahqAMAAAAAJBZ5wbLQZl6T5SJiNi4eTN1z1pcvlzduXpMlMnlcnG8v7x+qWL10u6gzNpC+hcCAAA0gaAMAAAAAJBZyYkyd5fvxvbOdqp+XVfGK+qDGpS5cydiO91/akREHEusX/rQ1UsRgjIAAEDLEJQBAAAAADJrbHCs9Ly5sxkPnj5I1a9jZCTah4ZK9cbERKp+tap29VKhEHHvXvr3He0rB2UePU2uXhqqPCgoAwAAtAhBGQAAAAAgs5KrlyIiZpZnUvXL5XIV65fWD+hEmYj6rF861ldevfToIyfKLKZ/GQAAQBMIygAAAAAAmZVcvRQRMbOULigTEdE1Xl6/tDFxK4rFYuqe1ap2okxExO3b6d93PLF66fFTq5cAAIDWJygDAAAAAGTW8Z7j0dvRW6qnl6ZT9+y8dLH0XFxdje1Hj1L3rNbZsxH5fHVnp9P/p1asXlrZ2Ir1ze1nRb43or38NUEZAACgVQjKAAAAAACZlcvlKtYvzS7Ppu7Zefp0Rb15717qntVqb484d+7F5yIipqbSv+9Yf1dF/ejHU2VyucqpMoIyAABAixCUAQAAAAAy7ezA2dJzPSbK5HcHZe7fT92zFufPV3euPkGZzor68cqHrF8SlAEAAFqEoAwAAAAAkGkXj5RXJc0sz8T61nqqfvlTpyrqZk6UiWhuUOZ4X+VEmYdPN8pF91D5eX0x/csAAACaQFAGAAAAAMi08eHx0vNOcSfuPLmTql9bb2+0D5enqRzUiTL370dsbLz43Ec5umuizKMPmyiz+jjdiwAAAJpEUAYAAAAAyLRkUCYi4ubCzdQ9k1NlCgd0okyxGDE7m+5dx/p2rV5KTpTpO1Z+fvow3YsAAACaRFAGAAAAAMi0cwPnorOtHPiYWJhI3TN/+nTpeavJE2UuXKj+bNr1S9359ujv6ijVFRNl+kbLz0/nniVzAAAADjhBGQAAAAAg0zraOuLS0KVSXe+gTOHe/Sg2MSRS7USZiPRBmYiIo4mpMg+TQZn+RFBmaz1iYzn9ywAAABpMUAYAAAAAyLzk+qWJxToEZRKrl4qrq7G9uJi6Z7VOnozI56s7OzmZ/n3H+stBmUcVq5dGKg8+nU//MgAAgAYTlAEAAAAAMu/K8JXS88O1h/F4/XGqfsmJMhERm/eat36prS1ibKy6s3UJyvR1lZ4fP/2QiTIRESsfpH8ZAABAgwnKAAAAAACZl5woExHx7uN3U/XLnz5VUW/evZuqX62qXb/03nvp33U8OVEmuXqpb3dQZi79ywAAABpMUAYAAAAAyLyrR69W1NcfXU/Vr/Ps2Yq6MHknVb9aXbpU3bkbNyK2t9O962hfOSjzcGUjisXis2L3RBmrlwAAgBYgKAMAAAAAZN5w93Cc7i+vS0oblGnr7a1Yv7Rx63aqfrW6dq26c2tr6dcvHesvr17a2NqJ1cKPkjfdQxFtHeWDJsoAAAAtQFAGAAAAADgUrh0rp0vSBmUiIjovl8e6bNxublDm9derP/vDH6Z7V3L1UkRi/VJbW+X6paeCMgAAwMEnKAMAAAAAHArJoMy9lXuxsL6Qql/Xpcul58KdO1FMu+OoBh/7WPVn0wZlkquXIiIe/v/s3WmMZfl5F/7n1nJv7UtX98x0d1Xv7RnP2I4TLyyRIAIUG0QEyGGReEEkEhLZBCEDAokIRAQRIBQiOQoJLxAIEgkpCBmMjADjKE5sw39IYnncs/Xe1dU9PV1L1173VtX9v+iee8+pqeXcc2q5k/p8JEvnd+vXzz0d5c2Uvv19ltaah4FTzedFq5cAAID2JygDAAAAABwLr4yla1iKtspUrjSDMvVqNWr37hWa14rnnosYG8t2t2hQZqy/kjrPvNcoE6FRBgAA+MARlAEAAAAAjoVko0xExPemiyVIKonVSxGHu36pVMq+fmm/Vy89Xkw2yiSCMouCMgAAQPsTlAEAAAAAjoXhynCc7j/dON+Zv1NoXvnSlqDM29cLzWtV1qDM669HrK/n/57RLauXZpaTjTKJ1UtLVi8BAADtT1AGAAAAADg2xgfHG8+TC5OFZnUO9EfXmWbw5jAbZSIiPvKRbPeq1YjJAn/V7s6OGKx0Nc5zy7XmD5ONMrXliLXF/F8EAABwCARlAAAAAIBj4+zA2cbz/cX7hedVLl5qPFfvFmuoadWHP5z97s2bxb4r2Sozs5RslHkufXHJ+iUAAKC9CcoAAAAAAMdGMijzaPlRVDequ9zeW/dEs6GmdvdeoVmtunRp7zvvKRyU6etuPM8lVy/1jqYvrj4p9kUAAAAHTFAGAAAAADg2kkGZetTjwdKDQvPKE+cazxuzs7GxeHirh8bHI7q7974XEVF0K1SyUWY2uXqpdyR9cWWu2BcBAAAcMEEZAAAAAODYGB8cT53vLxRbv5RslImIqN07vFaZzs6ICxey3S3eKJMIyiRXL/UMpy9qlAEAANqcoAwAAAAAcGwkG2UiIiYXJwvNK587lzpXDzEoE5F9/VLRoMxIYvXSbHL1Us+WRplVjTIAAEB7E5QBAAAAAI6Nk70no9zRbEe5v1iwUWZ8InWu3SsWvGnV5cvZ7hUNypxINMrMrdRiY7P+9KBRBgAA+IARlAEAAAAAjo2OUkecGTjTOBcNynQO9EfniRONc/Xe3ULzWpW1UWZmJmKuQNnLSH8zKFOvR8yv1J4eusoR3X3NiysaZQAAgPYmKAMAAAAAHCtnB5vrl+4vFAvKRER0T4w3ng+7USZrUCaiWKtMslEmImJmp/VLGmUAAIA2JygDAAAAABwr4wPNYMvt+dtRr9cLzStPnGs8V+/dKzSrVYcVlBnt606d51JBmcT6pVWNMgAAQHsTlAEAAAAAjpUPjX6o8bxYW4x7C8XCLalGmampqK+vF5rXilaCMjdu5P+e0f4tjTJLteahV6MMAADwwSEoAwAAAAAcK6+MvZI6f2/6e4Xmlccnmof19ag9fFhoXisGByNOncp2t1ijTDooM7tTo8yKRhkAAKC9CcoAAAAAAMfK1dGr0dXR1Thfm75WaF753ETqXGvT9UtFgjIju65e0igDAAB8cAjKAAAAAADHSrmzHFdHrjbORYMy3RPpoEz17uEGZS5fznavyOqlnu7O6Ct3Ns6p1UvJRplVjTIAAEB7E5QBAAAAAI6dl8debjy/Pv16bNY3c8/qeu65KJWbq4lqk+3ZKHP3bkSttve9nSTXL6UaZXq3NMrU6/m/BAAA4IAJygAAAAAAx04yKLNQW4h7C/nDLaWOjugeH2+cq/cmC71bq7IGZTY2IopshRrtb65fmllKrl5KNMpsVCNqK/m/BAAA4IAJygAAAAAAx84rY6+kzkXXL5UT65dqd+8WmtWqrKuXIoqtX0o3yiRXL42kL64+yf8lAAAAB0xQBgAAAAA4dq6OXo2ujq7GuWhQpjsRlKlOtmejTETEzZv5vycZlJlZ3qFRJiJidS7/lwAAABwwQRkAAAAA4Ngpd5bj6sjVxrlwo8y5ZlBmc34+NuYOLyxy5kxEpZLtbrGgTHP10lwyKNOrUQYAAPjgEJQBAAAAAI6ll8debjxfm74Wm/XN3LO6xydS5+q9w2uV6eiIuHgx291Cq5f6m40ys8u1qNfrTw9bG2VWNMoAAADtS1AGAAAAADiWkkGZxdpi3Fu4l3tWeWI8da7du5t7Vh5Z1y/dvp3/O5KrlzY26zG/uv700KNRBgAA+OAQlAEAAAAAjqVXxl5JnYusX+oeTwdlqpP3c8/KI2ujTJGgzEhi9VJEYv3S1kaZVY0yAABA+xKUAQAAAACOpaujV6Oro6txfn369dyzOnp7o/Pkyca5Nnl4q5ciIi5cyHZvejpicTHfd5xIrF6KiJhZehaUqQxGlDqbP7B6CQAAaGOCMgAAAADAsVTuLMf5wfON8+RisXBL+ezZxvNhB2XOn9/7znvu3Mn3HcnVSxERc8u1pw+lUkTvaPMHKzP5vgAAAOAQCMoAAAAAAMfW6YHTjecHiw8KzUquX6reb89GmYgCQZmdGmUiIvrGms/L0/m+AAAA4BAIygAAAAAAx9aZ/jON56mlqUKzkkGZ2tSDqG9sFJrXilYaZW7fzvcdo33dqfPssqAMAADwwSMoAwAAAAAcW8lGmZnVmVhdX809q3u8uXoparVYf/SoyKu15NSpiN7ebHfzNsr0dndGpav5K+V0UOZE81lQBgAAaGOCMgAAAADAsXW6/3Tq/HDpYe5Z5USjTEREbfLw1i+VStlbZfI2ypRKpRjta65fml2uNX+YCsrM5PsCAACAQyAoAwAAAAAcW1uDMkXWL3VPTKTO1cn7uWflceFCtnt5G2UiIkYS65fmdly9JCgDAAC0L0EZAAAAAODYOjNwJnUu0ijT/cILEZ2djfNhNspEHHyjTETEif5mo8zM0g5BmdpSRG0l/5cAAAAcIEEZAAAAAODYOtV7KjpLzXDL1GL+RplSV9fTsMwzhx2Uydoo8847ESs5cyzJ1UtzqdVLY+mLWmUAAIA2JSgDAAAAABxbnR2d8Xzf843zg6UHheYl1y9Vi1S35JA1KBMRcfduvu8Y7W+uXtqxUSYiYnk63xcAAAAcMEEZAAAAAOBYOz1wuvFcNChTuXSp8bx240bU6/VC81qRdfVSRMSdO/m+Y2ujTOPv13sifVFQBgAAaFOCMgAAAADAsXam/0zjucjqpYiI8pXLjefNxcVYf/So0LxWtNIok7fsJhmUqW5sxlJ14+mhb0tQZsXqJQAAoD0JygAAAAAAx9rEYHNd0tTiVCxWF3PPqly+kjqvXb+ee1arnn8+olze+15EgUaZxOqliIjZ99YvvW/1kqAMAADQngRlAAAAAIBj7eWxlxvP9ajHtelruWdVrqaDMtVDDMp0dGRfv5S3UWasv5I6P15ce/rQMxxR6mz+wOolAACgTQnKAAAAAADH2isnX0mdX5t+LfesrhMnonN0tHFeu34j96w8sgZl8jbKjA2kK2umF581ypRK6fVLgjIAAECbEpQBAAAAAI61k70n43T/6cb5tcf5gzIREZUrzVaZw1y9FBFx4UK2e3kbZU4NpBtlppfWmofk+iVBGQAAoE0JygAAAAAAx95HTn6k8fy9x98rNKt85XLjee3GjajX64XmtSJro8zUVES12vr80f50o8zjxcSQVFBmpvXhAAAAh0BQBgAAAAA49l4Za65fmlqaipnV/EGPyuVmo8zm/HysP3hQ6N1akbVRpl6PuHev9fndnR0x0tfdOD9eTDbKWL0EAAC0P0EZAAAAAODYSzbKRES8OfNm7lk9L7+cOq98r1hDTSuyNspERNy5k+87xhKtMtPJRpnKUPN5bSHfcAAAgAMmKAMAAAAAHHtnB86mzo9XHuee1fPhlyI6mr96XX3t8IIyWRtlIiJu3873HScHKo3n6aVEo0y5v/lcW843HAAA4IAJygAAAAAAx96JnhOpc5HVSx29vVG5fLlxXj3ERpkzZyK6urLdvX8/33ekgjLJRpnuvuZzVVAGAABoT4IyAAAAAMCx19fdF71dvY3z9Op0oXk9H2muclp97bWo1+uF5mXV2Rlx+nS2uw8e5PuOsYHm6qXHyaBMqlFmKeKQ/s4AAACtEJQBAAAAAIiIsZ6xxvP0SsGgzCuvNJ435uaidn+q0LxWnDmT7d5Uzlca6282yswsrcXG5rNATLJRpr4Zsb6a7wsAAAAOkKAMAAAAAEBEnOhtrl8qsnopIqL3I6+kzquvvVZoXisOPCiTaJTZrEfMLT9rlUk2ykRYvwQAALQlQRkAAAAAgNjfRpnKSy9FdDR//br65huF5rXioFcvnRyopM7TSzsEZWpL+b4AAADgAAnKAAAAAABExFhvIiizWiwo09HTE+Xz5xvntbfeLjSvFVkbZR48iNjcbH3+yUSjTETE48W1pw/J1UsRGmUAAIC2JCgDAAAAABARJ3rSq5fq9XqheZUPfajxvPbWW4VmtSJrUGZjI+Ldd1ufP7a1UWbxvUaZrUEZjTIAAED7EZQBAAAAAIj06qX1zfWYr84Xmld5sRmUqd27F5tLhxMcybp6KSLf+qWxnRplygPpi1YvAQAAbUhQBgAAAAAg0quXIoqvX+pJNMpERKy9fTjrl7I2ykRETE21Pn+w0hXlzuavlhuNMlYvAQAAHwCCMgAAAAAAkV69FBExvVIsKFPZEpRZPaT1S600yuQJypRKpTiZaJWZXnqvUWZLUEajDAAA0IYEZQAAAAAA4v2NMjOrM4XmdY+PR6mvGR5Ze+twGmXGxiK6u7PdzbN6KSJibKDSeH7caJTpT1+qCsoAAADtR1AGAAAAACAixnq2rF4q2ChT6uiIypUrjfPK7/1eoXlZdXRkb5XJ0ygTETGWbJRZfK9RZmtQxuolAACg/QjKAAAAAABExFB5KLo6uhrn6dViQZmIiL5PfbLxvPraa1GdnCw8M4sDD8r0Nxtlppfea5SxegkAAGh/gjIAAAAAABFRKpXiRM+JxvnxyuPCM4c++9nUeeG///fCM7M4cybbvTt38s0/mWiUebzwrFGmoyOiq7d5SaMMAADQhgRlAAAAAACeOdPfTJjcX7hfeF7PRz4S3ePjjfP8Vw8nKJP4yl29/XZEvd76/JMDzUaZpepGrFQ3nh7KiVaZqkYZAACg/QjKAAAAAAA8MzE40Xi+u3C38LxSqRRDf7LZKrP6ve/F+uxs4bl7uXo1273l5Yj7OfJAY4lGmYiI6aVnrTLd/c0PrV4CAADakKAMAAAAAMAzE0PNoMzDpYdR3agWntnzsY+lzuvvvlt45l5efDH73bfean3+WKJRJiJievHZ/53KiaCM1UsAAEAbEpQBAAAAAHjm3OC5xnM96jG5OFl4ZtfYydR5Y3q68My9fOhD2e/mCsr079Aok1y9VBOUAQAA2o+gDAAAAADAM8nVSxER9+bvFZ7ZdXIsdV5/fPBBmYmJiEpl73sR+YIyJ7c0yjxeeNYo050IylQXWx8MAABwwARlAAAAAACeSTbKRETcXbhbeGbniS1BmenHhWfu+Z2dEVeuZLubJyhzYkujzONGo4zVSwAAQHsTlAEAAAAAeGa4MhyD5cHG+d5C8UaZjv6+KPX0NM4b0zOFZ2aRdf1SnqBMuasjhnu7G+fpxWeNMsmgjNVLAABAGxKUAQAAAAB4plQqpVpl9qNRplQqRddYs1VmffrgVy9FZA/K3LwZUau1Pn9soNkqM734rFEmtXppqfWh1QmbbQAAIABJREFUAAAAB0xQBgAAAAAgYWJwovF8b754o0xEROfJZFDm4FcvRWQPymxsRNy61fr8k/2VxvP00jaNMoIyAABAGxKUAQAAAABIOD90vvF8b+FezK7OFp7ZdaIZlNl4fDiNMlevZr+bJyiTbJR5/N7qpWSjjNVLAABAGxKUAQAAAABI+OQLn2w816Me337w7cIzu5KNMjMzhedlcfly9rs3b7Y+f6SvGZSZW96mUaa2HLG52fpgAACAAyQoAwAAAACQ8APP/UD0dvU2zr99/7cLz+wcSzbKPI56vV545l5eeCGipyfb3TxBmdG+7sbz7HZBmQitMgAAQNsRlAEAAAAASCh3luOTzzdbZb419a3CwZausZON53qtFpsLC4XmZdHREXHpUra7+YIyzUaZ1dpmrFQ30quXIgRlAACAtiMoAwAAAACwxQ+e/cHG86OVR/H23NuF5iVXL0VErD+eLjQvq4MMyowkGmUinrXKbG2UqS61PhgAAOAACcoAAAAAAGzxB0//wdT59enXC83rPJEOymzMtF9QptXSnBP95dR5+6DMYmtDAQAADpigDAAAAADAFueHzkd3R7Mx5caTG4XmtXujzPx8xMxMa7NH+tJBmbnlWkRlMH1p7eBXTAEAALRCUAYAAAAAYIuujq64MHyhcb41d6vYvLEtQZlHjwrNyyprUCai9fVLo9utXqoMpS+tzrc2FAAA4IAJygAAAAAAbOPy8OXGc9FGmY7h4egYbLatrN1qMZWSUytBmRst/hVH+7auXqpF9GwJyqwJygAAAO1FUAYAAAAAYBuXhpspk8mFyVhdX809q1QqReXq1cZ57e23C71bVhcvZr97q8XSnKHe7iiVmue5pW0aZQRlAACANiMoAwAAAACwjUsjzaBMPepxZ/5OoXnpoMz1qNfrheZl0dcX8cIL2e5OTrY2u7OjFMO9zfVLs8s1q5cAAIC2JygDAAAAALCN5OqliIgbc8XWLyWDMptPnsT6o3cLzctqYiLbvVaDMhHp9Utzy9WIzq6I7r7mBY0yAABAmxGUAQAAAADYxvmh89FZ6mycbzzZv6BMRMTaW28VmpfV+Hi2e/futT57pC/ZKFN9+pBsldEoAwAAtBlBGQAAAACAbXR3dsfEYLOO5daTW4XmVa5eSZ3X3n670LyssjbK5AnKnEg0ysws154+9CSCMhplAACANiMoAwAAAACwg8sjzfVLRVcvdZ04EZ0nTzbO7RaUefw4YnW1tdkjW1cvRaQbZdYWWhsIAABwwARlAAAAAAB2cGn4UuP57vzdqG3UCs1Ltsqs3SgWvMkq6+qliIjJydZmjyZXLy09C8r0WL0EAAC0L0EZAAAAAIAdXBppBmXW6+txd+FuoXnlc+cbz7X79wvNyipro0xE6+uXRvubjTLzq+uxvrEZURlsXrB6CQAAaDOCMgAAAAAAO7g8fDl1vvnkZqF53eNnG88b09OxubJSaF4WrQRlWm2UGUk0ykREPFmppVcvaZQBAADajKAMAAAAAMAOLgxfiFKUGucbc8XWJZXPnk2da1NTheZlcfp0RKm0972IHI0yfeXUeXa5FtEz3Pxg7UlrAwEAAA6YoAwAAAAAwA56u3rjzMCZxvnmXMFGma1BmUNYv9Td/TQsk0XRRpm55Wq6UWZtIaJeb20oAADAARKUAQAAAADYxeWR5vqlwquXjiAoExExPp7t3v40yiSCMvXNiOpSa0MBAAAOkKAMAAAAAMAuLg1fajzfenIrNuubuWd1jo1FqaencT6soMzERLZ7d+60NvdE/9agTDWiMpi+tDbf2lAAAIADJCgDAAAAALCLi8MXG8/VzWo8XHqYe1apVEq1ylQPKShz7ly2ezdvtrYpaevqpdmlLauXIiJWBWUAAID2ISgDAAAAALCLc4PplMnt+duF5nWfPdN4rk0eTlDm0qW970RELC1FvPNO9rmVrs7oK3c2zu9bvRShUQYAAGgrgjIAAAAAALu4MHwhdb4z3+J+oi2SjTKHtXrp8uXsd2/caG32aF9z/dLcskYZAACgvQnKAAAAAADsYqxnLPq6+hrnu/N3C80rJ4IyGzMzsbm8XGheFgcZlEmuX5pdrkb0DKcvaJQBAADaiKAMAAAAAMAuSqVSnB863zgXXr105kzqXHvYwq6jnM6fjyiVst29ebO12clGmdnlWkRlMH1BUAYAAGgjgjIAAAAAAHu4MHSh8Vx09VLXc8+lzuvvvltoXhaVSsTERLa7RRplrF4CAADanaAMAAAAAMAezg2dazzfX7wftY1a7lldp06lzocRlInIvn6p1aDM+xplunsiOpufaZQBAADaiaAMAAAAAMAekquXNuubMbk4mXvW+4Iyjx7lntWKAwvK9DdDMXPL1ajX6xE9w80Lq09aGwgAAHCABGUAAAAAAPaQXL0UEXHrya3cszr6+qKjv79xbrdGmUePIhYWss8dTaxeqm3UY6m6EdEz0rywMpd9GAAAwAETlAEAAAAA2MOlkUup85szbxaal2yVabegTETEzZvZ7yZXL0VEzC5VI3oTQZlVQRkAAKB9CMoAAAAAAOyhv7s/1SpzbfpaoXntHpRpZf3SSKJRJiJidrlq9RIAANC2BGUAAAAAADL48IkPN56vzRQMyjz3XOP5gx6UeV+jzHLN6iUAAKBtCcoAAAAAAGTw4bFmUObR8qN4vPI496yjaJQZHo4YG8t2t0hQZm7Z6iUAAKB9CcoAAAAAAGTw8tjLqfMbM2/knpUMymwuLMTmykruWa3I2irT0uql/i2rl5aq6UYZq5cAAIA2IigDAAAAAJDBSydeSp1fn34996yu506lzu22fqmVoMxgpSu6OkqN89PVS8PNC+urEbXV7AMBAAAOkKAMAAAAAEAGw5XhODtwtnF+faZAUOZUewdl7t6NqNWy3S2VSjHU22yVmV+tpVcvRVi/BAAAtA1BGQAAAACAjD40+qHG8425FmpXtmj3oMzGRsSdO9nnDieCMk9WaunVSxHWLwEAAG1DUAYAAAAAIKMrI1caz3fn70ZtI2PtyhbvC8o8elTovbLKGpSJaG39UqpRZmXL6qWIiBWNMgAAQHsQlAEAAAAAyOjSyKXG83p9Pe7Mt1C7ktAxOBgdAwONc/XO3cLvlsVBBWXe1yhj9RIAANCmBGUAAAAAADJKNspERFx/cj3XnFKpFOVLzdBN9dbNQu+V1enTEb292e4WCspYvQQAALQpQRkAAAAAgIwuDF2IjlLz16o35lpIk2xRuXih8bx263b+l2pBqRSRyOfsqrWgTFfjedtGGauXAACANiEoAwAAAACQUU9XT4wPjDfORYIy5YuJNU4PHsTm0lKhd8sq6/qlQo0y5cGIKDUvWL0EAAC0CUEZAAAAAIAWXB5pJk0KBWUuXUyd127fzj2rFVmDMjdvRtTr2e4mgzKrtc1Y26xH9Aw3L1i9BAAAtAlBGQAAAACAFlwZudJ4vjt/N2obtVxzKhfTQZnqzVuF3iurrEGZ5eWIhw+z3R3q6U6d37d+yeolAACgTQjKAAAAAAC04NJIYmVSfT3uzN/JNaf7/PmIjuavaKu3Dicoc+nS3nfek3X9UrJRJiJifqW2pVFGUAYAAGgPgjIAAAAAAC1INspERFx/cj3XnI5yObonxhvntVs3C71XVlkbZSLyB2WerNQiehKNMlYvAQAAbUJQBgAAAACgBReGLkRHqfmr1RtzGdMk26hcbNa7rL35VqH3yurChVSRza6yBmWG3tcos271EgAA0JYEZQAAAAAAWtDT1RPjA80mmCJBmZ6XX248V2/ejI25gw+UlMsRExPZ7hZqlOkdbX6wMpPx7QAAAA6WoAwAAAAAQIsujzT3FxUJyvR+4gdS5+Xf/d3cs1qRdf1S5qBM3zZBmb6x5gfL0xH1esa3AwAAODiCMgAAAAAALboycqXxfHf+btQ2arnm9H7fx1N7kFZ+p72CMrduZbs3UO6KjlLz/DQoc7L5wfpqRHUp+wsCAAAcEEEZAAAAAIAWJRtl1uvrcWf+Tq45nQP9UXnxxcZ5+Xd/p/C7ZZE1KPPoUcTi4t73OjpKMZRYv/RkpRbRfzJ9aflxC28IAABwMARlAAAAAABalAzKRERcf3I996y+7//+xvPqd1+LerWae1ZWly5lv5u1VWZ4a1AmuXopImJpOvuXAgAAHBBBGQAAAACAFl0cvhgdpeavV2/M3cg9q/cHfqDxXF9bi9U33yz0blm0EpS5eTPbvfcFZTTKAAAAbUhQBgAAAACgRZXOSkwMTjTOhYIy3/ex1Hnlu9/NPSurgwjKDPVsbZTZEpRZEpQBAACOnqAMAAAAAEAOl4abaZMiQZnu8fHoHB5unFe/+1qh98pidDRiZCTb3TyNMvMaZQAAgDYlKAMAAAAAkMOVkSuN57vzd6O2Ucs1p1QqRc9HPtI4r7528I0yEdlbZTI3yvR2NZ4XVtcjuioR5cHmBY0yAABAGxCUAQAAAADI4fLI5cbzen097szfyT2r56PNoMzajZuxubRU6N2y2O+gzEClGZRZXFt/+tA/1rywPJ3xzQAAAA6OoAwAAAAAQA7JoExExPUn13PP6v3oR5uHzc1Yff313LOyyhqUuXUrYnNz73sDlebqpcW19ajX6xF9ifVLGmUAAIA2ICgDAAAAAJDDxeGL0VFq/or1xtyN3LOSq5ciIlZeey33rKyyBmXW1iKmpva+N9jTbJTZ2KzHSm0joj8RlFkWlAEAAI6eoAwAAAAAQA6VzkpMDE40zkWCMt3PPx+do6ONc/X27SKvlknWoExExN27e98ZSARlIiIWVtc1ygAAAG1HUAYAAAAAIKeLwxcbz7fnbxea1X2uGbqp3b1XaFYWFy5kvzs5ufedwco2QZn+seYHy9PZvxAAAOCACMoAAAAAAOR0YehC4/nu/N3YrG/mnlWeONd4rt47+KDM2bPZ72Z5ncGe7tR5cW1Lo0x1MaK2mv1LAQAADoCgDAAAAABATueHzjee1zbW4uHSw9yzyslGmampqNdqhd5tL319EWNje9+LyBaUef/qpVpE/8n0pWXrlwAAgKMlKAMAAAAAkFOyUSai2Pql7kSjTGxsRO3Bg9yzshofz3Yvy+qlgS2rlxZXtzTKREQsCcoAAABHS1AGAAAAACCnC8MXUuc783dyz0o2ykREVO8e/PqliYm970Rka5QZ2toos7Ye0b+lskajDAAAcMQEZQAAAAAAchrrGYuB7oHG+faT27lndW9JrdTu3c09K6usQZlMjTLvW720XaPMdMY3AwAAOBiCMgAAAAAAOZVKpTg/dL5xLtIo03XqVJR6exvnw2iUybp66cGDiFpt9zu93Z3R2VFqnBdX1yP6twRlNMoAAABHTFAGAAAAAKCAZFDm9vzt3HNKpVKUE8mVahs1ytTrEVNTu98plUoxUGm2yiyu1SK6+yK6epqXlgRlAACAoyUoAwAAAABQwIXhC43nqcWpqG5Uc8/qPneu8Vxro0aZiIzrlxJBmYXV9YhSKb1+SaMMAABwxARlAAAAAAAKGB9opk3qUY+HSw9zzyqPn20816amol6vF3q3vWRtlImIuJchtzPYkwjKrK0/fegfa15Yms7+hQAAAAdAUAYAAAAAoIAzA2dS56mlPXYU7aL7THPW5uJibM7P556VRSuNMi0HZVafBWU0ygAAAG1EUAYAAAAAoIAz/emgzIPFB7lndZ89mzrXpvKHbrLo6Yk4eXLvexERb721953k6qXF1drTh/7EFywJygAAAEdLUAYAAAAAoIBTfaeis9TZOD9Y2segzP37uWdldfFitnuvv773nYGe7sbz4ppGGQAAoP0IygAAAAAAFNDV0RXP9T3XOE8t7s/qpYiDb5SJiHj55Wz3rl2LqNd3v7Pt6qX+seaF1ScRG7UW3xAAAGD/CMoAAAAAABR0uv9047lIo0zH0FB0DAw0zofRKPPhD2e7Nzsb8c47u98ZTK1e2qZRJiJiebqFtwMAANhfgjIAAAAAAAWdGWg2wRRplCmVSqlWmXZqlInYe/3SQDIoU12Pzc16RP+WoMyS9UsAAMDREZQBAAAAACgo2SjzcPlhbNY3c8/qPnu28Vw9hEaZVoIy167t/vPk6qV6PWKpur5No4ygDAAAcHQEZQAAAAAACko2yqxvrsfjlfxhkGSjzPr9g2+UuXAholLJdnevoMxAT3fqvLi2rlEGAABoK4IyAAAAAAAFnek/kzoXWb+UbJTZePIkNhaXcs/KorMz4qWXst1tZfVSRMT8ynpE31j60vJ0C28HAACwvwRlAAAAAAAKemHghdT5/mL+lUnJRpmIiNq9u7lnZZV1/dL167v/fGygnDpPL65F9AxHdCSaZjTKAAAAR0hQBgAAAACgoPGB8ehOhEFee/xa7lmVy5dS59U338w9K6sXX8x2b2oqYmNj55+fHEjvcHq8VI0oldKtMsuCMgAAwNERlAEAAAAAKKjcWY6Xx5q1LL/36Pfyz7p4MUrlZjPL2ptvFXq3LM6dy3ZvYyPi4cOdf35yS6PM44W1pw/9J5sfapQBAACOkKAMAAAAAMA++P7nvr/x/MbMG7GyvpJrTqmrKypXrjTOa2++Ufjd9jIxkf3uvXs7/2yg0hWVruavnR8vCsoAAADtRVAGAAAAAGAffPzUxxvP6/X1YuuXEruQVt94M+r1eqF328v4ePa7k5M7/6xUKqXWLzWCMn2JoIzVSwAAwBESlAEAAAAA2Aff99z3pc5F1i/1vNQMymzMzMT6u+/mnpXFfjXKREScHEwGZapPHzTKAAAAbUJQBgAAAABgH5zsPRnnBs81zt959zu5Z1VefCl1XnvzrdyzsujvjxgdzXZ3r6DMqYFy43nbRpmV2YiN9RbfEAAAYH8IygAAAAAA7JNXTr7SeH5z9s3ccyovfih1Xnvzjdyzssq6fmm31UsREWP9iUaZhWdBmWSjTNQjVmZaezkAAIB9IigDAAAAALBPXhxtrkx6uPQwnqw9yTWna3Q0up5/vnFefSN/6CarrOuX9l69lGyUqUa9Xt8SlAnrlwAAgCMjKAMAAAAAsE9ePPFi6vzWbP6VSZWXmrMOo1Fm34IyA81GmerGZsyvrqdXL0VELAvKAAAAR0NQBgAAAABgn3xoNL0yqUhQpufFlxrPazdvxWa1mntWFllXLz14ELG+vvPPk0GZiIjHi2saZQAAgLYhKAMAAAAAsE9O9Z6K0cpo4/zmTP6VST2JRpnY2Ijq9etFXm1PWRtlNjefhmV28r6gzMJaRN9Y+pKgDAAAcEQEZQAAAAAA9kmpVIoPnWi2yrw5mz8oU3kxvcZp9Y38s7LIGpSJ2H390qnBcur8eLEa0TMS0dHV/NDqJQAA4IgIygAAAAAA7KPk+qXrs9djfXOXPUW7KJ8/H6VKs51l7c03Cr/bbrKuXoqImJzc+Wfbrl7q6Ei3ymiUAQAAjoigDAAAAADAPkoGZaqb1XiwuMueol2UurqicvVq47z65luF3203rQRldmuUGe7tju7OUuP8eHHt6UPfyealpXdbfDsAAID9ISgDAAAAALCPLgxdSJ1vz9/OPavyYjN0s3bjeu45WfT1RYyN7X0vYvegTKlUipG+5vql2eXq04f+xPDl6RxvCAAAUJygDAAAAADAPjo/dD51vjN/J/es8vkLjeeNdx/H5vJy7llZZG2V2W31UkTEaF9343l2ufb0of9U84LVSwAAwBERlAEAAAAA2EcjlZEYKg81zkUaZcrnJlLn6r09EioFTUzsfSdi90aZiEg1ysw1GmUSQZnFd1p8MwAAgP0hKAMAAAAAsI9KpVJq/VKRRpnuLcmV2r27uWdlsV9BmVSjzNKzRpnBF5oXVuciaistvh0AAEBxgjIAAAAAAPssuX7p7nz+cEv53LnUuXp3j4RKQVlXLz18GFGr7fzz0USjzJOV94Iyp9OXFh62+HYAAADFCcoAAAAAAOyzZFDmwdKDWF1fzTWnc3AwOkdHG+dqmzTK1OsRU1M7/zy5emn2vdVLgjIAAEAbEJQBAAAAANhn54ebQZl61OPeQv4mmO5zzfRK7U57BGUidl+/lFy9tFzdiLX1jW2CMg9afDsAAIDiBGUAAAAAAPbZ+cHzqfPt+du5Z5UnmuuXqrulU/ZB1tVLERGTkzv/LLl6KSJibrkWMfhC+pJGGQAA4AgIygAAAAAA7LPk6qWIKNQoU042ykxNRb1Wyz1rL60EZXbL7IwkGmUinq1fqgxGdPc3P9QoAwAAHAFBGQAAAACAfdbX3Rcnek40zoVWLyUaZWJjI2oPDi5g0tMTcepUtru7rl7qTzfKzC7VIkqliKHE+iWNMgAAwBEQlAEAAAAAOAATg80mmMmFXfYU7aE8ka55qU0dbBPLxMTedyL2Wr2UbpSZW64+fRhMBmU0ygAAAIdPUAYAAAAA4ACMDzYDLkUaZbpeeCF1Xn/nYJtYsq5f2n310pZGmeVn66IGE38XQRkAAOAICMoAAAAAAByAZKPMw6WHUdus5ZrT9dxzqXPt4TuF3msvWRtldg3K9KYbZWYbjTLJoIzVSwAAwOETlAEAAAAAOADjA81qlo36RjxczBcM6SiXo3NsrHE+6EaZrEGZd96JqFa3/1lXZ0cM9nQ1ztuuXqouRqwt5HxLAACAfARlAAAAAAAOQLJRJiLi3mL+9Uvdzz/feD7oRpmsq5ciIu7f3/lno4n1S83VS6fTl7TKAAAAh0xQBgAAAADgAIwPphMnkwuTuWd1vdBcWbT+sD0aZSJ2X7802tdcv9RolBl4Pn1p8VELbwYAAFCcoAwAAAAAwAE41XsqKp2VxrlIUKb7hUSjzDsH2yjTSlBmcpe/0sh2jTI9w+lLa/MtvBkAAEBxgjIAAAAAAAegVCrF+ECzVebeQv7VS13PNxtlNqanY7NaLfRuuzlzJvvdrI0ys+81ylQG05fWFlp4MwAAgOIEZQAAAAAADsjEYLOe5c7Cndxzko0yERHrjw5uZVGlEvH883vfi9g9KJNslJlrNMoMpS+tPmnx7QAAAIoRlAEAAAAAOCAXhy82nu88uRMbmxu55iQbZSIi1h8+LPRee8m6fmm31UujqaBMNTY36xGVLUEZq5cAAIBDJigDAAAAAHBAkkGZ6mY1ppamcs3Z2ihTe/hOoffay/j43nci9li91N9cvbRZj1hYXY/o6IwoDzQvrQrKAAAAh0tQBgAAAADggCSDMhERt57cyjWna8supPV32qNRJuvqpYiI2eXq04fKYPPDtYUW3wwAAKAYQRkAAAAAgAOyX0GZjt7e6BwZaZyruyVU9kHWoMy770asrm7/s9G+7tS5GZRJrF+yegkAADhkgjIAAAAAAAdkuDIcJ3pONM55gzIREeWLiTVON24Weq+9ZF29FBFx//72n49uaZSZW649fehJBGWsXgIAAA6ZoAwAAAAAwAFKtsoUCcpUrlxuPK/duFHonfaStVEmYuf1SyMaZQAAgDYkKAMAAAAAcID2KyhTvtwMymzMzMT67Gyh99pNK0GZycntP9/aKDP7XqNMZbD54dpCi28GAABQjKAMAAAAAMABujjUDMrMrs3G3OpcrjmVRFAmIqJ6gK0yZ85ElErZ7u7UKNNX7oxyZ/NX0HPvNcpYvQQAABwhQRkAAAAAgAN0buhc6nx/8X6uOVuDMmvXDy4o090d8cIL2e7uFJQplUqp9Uvbr156kvMNAQAA8hGUAQAAAAA4QGcHzqbOk4s77CraQ9fp09HR19c4rx1go0xE9vVLO61eikivX2quXkoGZRYi6vUcbwcAAJCPoAwAAAAAwAHaGpTJ2yhTKpWinGiVqd64Xui99jI+nu3eTo0yEZFqlNl29VJ9M6K6lOPtAAAA8uk66hfg/W7duhWvvvpqTE1NRbVajTNnzsRLL70Un/jEJw7tHZaWluK1116LN954Ix4/fhyrq6sxMjISp0+fjk996lMxkfWfkwAAAADAMdfX3Rcnek7EzOpMRETcX8gXlImIqFy6FKvf/W5ERKy++VbU6/UolUr78p5bZf0V4G5BmVSjzNI2jTIREWvzEZWBFt8OAAAgH0GZNvI//+f/jH/wD/5BfPvb397255cvX46/+Tf/ZnzhC184kP/4/e53vxu//uu/Hv/jf/yPePXVV2N9fX3Hu6+88kp84QtfiJ/4iZ+Iri7/bwQAAAAAuznTf6YZlFnKH5Tp+ehH48mXvxwRERvT01G9fTsqFy/uyztulTUoMz0dsbIS0dv7/p/t2SgTEbE6HzF0JudbAgAAtMbqpTbxxS9+MT7zmc/sGJKJiLhx40b89E//dPzxP/7H48mTJ/v6/T/zMz8TH/vYx+Jnf/Zn49vf/vauIZmIiO9973vx+c9/Pv7AH/gDcf36wVa8AgAAAMAH3dnB5vqlIo0yfZ/6ZOq8/OqruWftpZVS6cnJ7T8fSTbKLL/XKDOYvrS20OKbAQAA5KcKpA38zM/8TPzLf/kvG+fu7u740R/90fj0pz8dlUolrl27Fr/6q78as7OzERHx9a9/PT73uc/FV7/61eju7t5pbEsWFxdT53K5HJ/61KfiD//hPxxnz56NoaGhePz4cXzzm9+Mr3zlK40gze/8zu/ED/3QD8U3vvGNuHhA/3IFAAAAAD7ozg40gzJTi1OxWd+MjlLr/46xcvVqdAwNxeb8fERErLz6aoz++T+/b++ZND6e/e69exFXr77/89FEo8xKbSNWaxvR877VS/v7jwIBAAB2IyhzxH7jN34j/sk/+SeN84ULF+K//bf/Fi+//HLq3j/+x/84Pve5z8XXvva1iIj42te+Fj/3cz8X//Af/sN9fZ9Pf/rT8eM//uPxF//iX4yhoaFt79y4cSP+0l/6S/Hqs3+tcv/+/fjxH//xxrsBAAAAAGnJoEx1sxqPVx7Hc33PtTyn1NERfZ/4RCx+/esREbGvIllsAAAgAElEQVT8/7VHo8z9HUpyRhONMhERc8u1eKFnOH1pdb7FNwMAAMjP6qUjVK/X4+/8nb/TOJfL5fgv/+W/vC8kExExPDwcX/7yl+Py5cuNz/7Fv/gX8ejRo315l49//OPx1a9+Nf7P//k/8RM/8RM7hmQiIi5fvhz/63/9r7hy5Urjs//9v/93/NZv/da+vAsAAAAA/H4zPpCuZ7m/WGD90ieb65dqU1NRm5rKPWs3L7wQ0ZHxN8j37m3/+UhfuhF7Zqka8b5GGUEZAADg8AjKHKHf/M3fbLSyRET85E/+ZHz0ox/d8X5/f3/8s3/2zxrnxcXF+JVf+ZV9eZcf+7Efi89+9rOZ7w8PD8c/+kf/KPXZf/2v/3Vf3gUAAAAAfr85M3AmdZ5cmMw9q/cHvj91Xn3zzdyzdtPd/TQsk8XkDn+dE/1bG2WqEZXB9KW1hRxvBwAAkI+gzBH6T//pP6XOP/VTP7Xnn/kzf+bPxAuJ/zrdOuMw/Yk/8SdS5xs3bhzRmwAAAABAezszcCY6Ss1fx95duJt7VuXSpdS5evtO7ll7GR/f+07EzkGZ0S1BmdnlWkR3T0Rn4nOrlwAAgEMkKHOEvvKVrzSeJyYmtl25tFVXV1cqoPKd73wn7u3Ua3rABgfT//JjcXHxSN4DAAAAANpdubMcE4MTjfONufz/6KxzeDg6T5xonKu3bxd5tV0VDsr0pYMyM8vVpw/J9UurT3K8GQAAQD6CMkdkfn4+bt261Tj/4A/+YOY/u/Xud77znX17r1bcvHkzdX4haw8rAAAAABxDl4abTTA3527ucnNv5QsXGs/tHJQZ7u2OUql5nl16FpTpHW1+uDKb7+UAAAByEJQ5IteuXUudr169mvnPbr27ddZh+fVf//XU+Q/9oT90JO8BAAAAAB8El0cuN57vzN+J2mYt96x2C8q8+27E6ur7P+/sKMVIb3fjPPteo0xfsxEnVmYKvCEAAEBrBGWOyNY2lvPnz2f+s1vvbp11GJ48eRL/6l/9q8a5XC7Hn/2zf/bQ3wMAAAAAPiiSjTLr9fW4N59/pXoyKLP+zjuxubRU5NV2lDUoExExNbX956P9zfVLzUaZRFBmWVAGAAA4PIIyR2R+fj51PpHYKbyX0dHR1HnrrMPw0z/90/HOO+80zp///Ofj+eefP/T3AAAAAIAPimSjTETEjSc3cs8qX0j/Y7rq3bu5Z+2mlaDMTuuXRvuaQZmZ5WctOhplAACAIyIoc0QWFxdT597e3sx/tq+vL3VeWFjYl3fK6pd+6Zfi3//7f984X7p0KX72Z3/2UN8BAAAAAD5oLg5fjFKUGucbcwWCMucvpM4HtX5pv4Myc++tXupN/GPA5dkcbwYAAJBP11G/wHG1srKSOpfL5R1uvl+lUtl11kH6yle+En/jb/yN1Lv8x//4H2NwcHBf5v/8z/98/PzP/3ymuw8ePNiX7wQAAACAw9Db1RtnBs7E/cX7ERFxcy7/SvXy+XOp89qtW4XebSdnzmS/u1NQ5kR/d+N55r3VS8lGmbUnERvrEZ1+XQ0AABw8/+VxRLY2yFSr1cx/dm1tbddZB+XrX/96/IW/8BdiY2MjIiI6OzvjV3/1V+OTn/zkvn3H/Px83L9/f9/mAQAAAEA7uTR8qRGUuTWfP9zS0dMT3WfORG1qKiIiqjcPJihTqUQ891zEo0d7371zZ/vPk40ys+8FZXq3rKJfmY0YOJXzLQEAALITlDkiAwMDqXMrrTBb7+5Xm8tuvvGNb8SP/MiPNL67o6Mj/t2/+3fxuc99bl+/Z2hoKM6ePZvp7oMHD2Jzc3Nfvx8AAAAADtK5oXMRz/6d2OTCZNTr9SiVSrv/oR2Ur1xuBGXWbuRf47SXiYlsQZmdSm1G+5tBmaXqRqytb0QluXopImJlRlAGAAA4FIIyR2RoaCh1np3Nvod3ZmYmdT7ooMw3v/nN+FN/6k/F0tJSRDwNyfybf/Nv4i//5b+879/1xS9+Mb74xS9mujs+Pq59BgAAAIAPlPGB8cbzYm0x5qvzMVwZzjWrcvlKLP3mNyIionrzZtQ3NqLU2bkv75l08WLE//t/e9/bKShzoi+9dn5uuRbP921plFlO/84TAADgoHQc9QscV5cuXUqd7+zUS7qNrXcvX768L++0nW9+85vx2c9+NhYXFyMiolQqxb/+1/86/spf+SsH9p0AAAAA8PvV+OB46jy5MJl7VuXKlcZzfW0tapP5Z+1my68yd3TrVsR2BdAjfd2p88xSdZvVS4IyAADA4RCUOSIvv/xy6vz2229n/rPXr1/fddZ++da3vhWf/exnY2FhISKehmR++Zd/Of7qX/2rB/J9AAAAAPD7XbJRJiLi3uK93LMqV9L/gG5ty+8N90vWoMzaWsSDB+///ER/ulFmdrkaoVEGAAA4IoIyR2RoaCguXrzYOP/2b/925j/7W7/1W6nzxz72sX17r/d861vfis985jOpkMwv/dIvxV/7a39t378LAAAAAI6Ls4NnU+cijTLly1dS57XrN3LP2k3i15h7unnz/Z+Nbg3KLNU0ygAAAEdGUOYI/ek//acbz3fv3o1r167t+WfW19fja1/7WuP8sY99LM6dO7ev7/Xtb3871SQTEfGLv/iL8VM/9VP7+j0AAAAAcNz0dvXGyd6TjXORoEznQH90nT7dOB91o0zE0/VLW432pYMyM8vViO6eiO6+5ocaZQAAgEMiKHOEPve5z6XOv/zLv7znn/nyl78cDxL9pVtnFPV//+//jc985jMxPz/f+OwXf/EX4/Of//y+fg8AAAAAHFfJ9UuTi/mDMhERlSvNVpm1GwcTlDl3LqIj42+St2uUGe7tjlKpeZ5bqj59SLbKaJQBAAAOiaDMEfojf+SPxCc+8YnG+Vd+5Vfitdde2/H+0tJS/N2/+3cb5/7+/vjJn/zJHe//2I/9WJRKpcb//u2//be7vs+rr74aP/zDP5wKyXzpS1+KL3zhCxn+NgAAAABAFuODzaDM/YX7hWZVLl9uPFdv3Y56vV5o3nbK5YiJiWx3twvKdHaUYqS3u3GeWX4WlOkbbV7SKAMAABwSQZkjVCqV4p//83/eOFer1fiRH/mReP31199398mTJ/Hn/tyfixs3mnuG/9bf+lvx/PPP78u7/O7v/m788A//cDx58qTx2Ze+9KX463/9r+/LfAAAAADgqWRQ5sHSg1jfXM89q/tcM8FSX1mJjZmDCZxcvJjt3nZBmYj0+qXZbRtlZnO+GQAAQGu6jvoFjrs/9sf+WPy9v/f34p/+038aERG3b9+Oj3/84/GjP/qj8elPfzoqlUpcu3Yt/sN/+A8xO9v8j8Uf+qEfir//9//+vr3H3/7bfzs1v7u7O37hF34hfuEXfqGlOdcPaA8yAAAAAPx+cXbgbON5o74R7yy/k/qsFeXx8dS5du9edI2NFXq/7Vy6FPEbv7H3vVu3tv98tL8c8XgpIiJml2tPP+xLBGU0ygAAAIdEUKYN/NzP/VwsLS3Fl770pYh42izza7/2a/Frv/Zr297/o3/0j8Z//s//Ocrl8rY/z2NjYyN1rtVqqfYaAAAAAGB/PNf3XOr8eOVx7qBM93h6J1L13mT0fvz/Z+9OY+xKz/vAP7eWWztZVdyaay0km2x1N1tiq1tqeSTHsSwbtqwE8WCcGAGcQeDEg8GM4fkQ28BsHyaDYDJLLAwCZwJMBDsGJh57BrGNkWRZkW1Zkrvd3VJvZDe3quJSXIos1r7cW1V3PlB97r3VZPHUvafIYtXv94Xvc+573vP2RxX+ep6P13y3BxkcTLdvdDRifj6ira36eVVHmbn7dZQRlAEAAB4No5c2gVwuF1/+8pfj61//erz88ssP3Dc4OBi/+Zu/Gd/61reiu7v7Ed4QAAAAAMjKnrY9VfXtuds1n9V88EBVXbx2teaz1pI2KBMRMTz80Wc97c3JevzD0UutO8obFmdquxgAAMA66SiziXzhC1+IL3zhC3Hp0qV4/fXXY3R0NAqFQuzfvz+eeeaZ+OQnP7mu877yla/EV77ylVR7/yxN31QAAAAAoG6723ZX1bfnaw/KNLS0RNO+fbF082ZERBSuXKnrbg+ynqDMpUsRzzxT/ay3o9xRZuLD0Uv5jvKG4mzEykpEg/9vJwAAsLEEZTahwcHBGFzP//IEAAAAAJ4YO1t2RlOuKZZKSxERMTY/Vtd5zYcOJUGZ4tVrdd/vfgYG0u+9dOmjz3oqgjIzi0uxuLQcLfmu6k3F2YiWVc8AAAAyJp4PAAAAAPAINeQaYlfbrqSup6NMRET+0KFkXdygjjJ79kR0dDx8X0TE0NBHn/W256vqiblidUeZiIjCbI23AwAASE9QBgAAAADgEascv1RvUKa5Mihz40aUisW6zrufXC79+KX7dZTpbm+uqu/OFSJaOqs3Lc7UeDsAAID0BGUAAAAAAB6xPW17knXdQZnD5aBMrKxE8fr1us57kHqCMr0d1R1lxmcLEflVQZmCoAwAALDxBGUAAAAAAB6xjRq9FBFRuLwx45fWE5Qplaqf9awKytydLd4nKGP0EgAAsPEEZQAAAAAAHrHK0Ut35u/ESmml5rOajxypqgsjwzWftZaBgXT7Zmcjbq/K/vS0rwrKzBUi8h3Vm3SUAQAAHgFBGQAAAACAR6xy9NJSaSkmFydrPqtpz55oaG9P6sLISF13e5C0HWUiPjp+aWdbc+Ry5frubCGixeglAADg0ROUAQAAAAB4xCo7ykREjM2P1XxWLpeL5r6+pN6MQZnGhlx0tzUn9fhc4aOjlxYFZQAAgI0nKAMAAAAA8Ijtbq8Oytyev/2AnenkK4IyxeGNCcr096ffuzooE1E9fmlirvjRoExhtraLAQAArIOgDAAAAADAI7a6o0zdQZn+io4y165FaWmprvPup60tYv/+dHuHhj76rKejHJQZny1ENLdFRMU8JqOXAACAR0BQBgAAAADgEVsdlLkxe6Ou8/J9/eViaSmK167Vdd6DpB2/9PCOMoWIXK66q4ygDAAA8AgIygAAAAAAPGItjS2xv6PcnuXixMW6zqscvRQRURjZmPFL9QVlmpP1+Fzh3qKlMihj9BIAALDxBGUAAAAAAB6Do91Hk/WFiQt1nVU5eikiojD8eIMyV65EFIvVz3orRi/dnf3hj/mO8oZFHWUAAICNJygDAAAAAPAYHO8+nqyHJoeiuFJcY/faGnt6oqGrK6kLw8P1XO2B0gZlVlYiLl+uftZTEZSZWVyKwtKK0UsAAMAjJygDAAAAAPAYHOs5lqyLK8W4MnWl5rNyuVzkBweSevFifaOcHmRg4OF7PrR6/FLl6KWIiIm5gqAMAADwyAnKAAAAAAA8Bse6j1XV5yfO13Vey7HyeYsX6hvl9CBpO8pE3C8ok6+qx+cKES2VQZnZOm4GAACQjqAMAAAAAMBjMLhzMHKRS+qLE/V1gWk5Xh7ltHznTiyNj9d13v3s3x/R0pJu7+qgTG9HdVDm7mwxIt9RfiAoAwAAPAKCMgAAAAAAj0FrU2sc2XEkqS9M1NcFpuXY8ap68Xz2XWUaGtKPXxoZqa57VgdlVo9eWjR6CQAA2HiCMgAAAAAAj0nl+KXzd+scvXR8dVCmvvMepL8/3b7Ll6vrj4xeml0VlCkIygAAABtPUAYAAAAA4DGpDMpcnr4ci8uLNZ/VtHdPNOzYkdSLFzYmKHPkyMP3RHw0KLOzrTly5UlTMTFXiGgRlAEAAB4tQRkAAAAAgMfkWE85KLNSWomhyaGaz8rlclVdZTZi9FJE+qDM6GhEsViuGxty0d3WnNTjs8WIfEd5w3IhYqmQ0S0BAADuT1AGAAAAAOAxOd5dPS6p/vFL5eDN4oXHG5QplSKuXat+Vjl+6e7cqtFLEbrKAAAAG05QBgAAAADgMTmy40g0NTQl9YWJ+sItLYODyXplcjKWJybqOu9+0gZlIj46fqmn42FBmdk6bgYAAPBwgjIAAAAAAI9Jc0NzDOwcSOp6gzLNq1IshdVJlQzUFZSp7CgzW4hoEZQBAAAeLUEZAAAAAIDH6Fh3eVzShbv1BWXyR/qq6sLISF3n3c/BgxG5XLq9Hw3KNCfr8blCRL6jeoPRSwAAwAYTlAEAAAAAeIyOdx9P1qOzozFTR1gkf+hgREP5z76Fkew7yuTzEfv3p9u7OijTWzF6aWK2GNHSVb1hPvtRUQAAAJUEZQAAAAAAHqOj3Uer6qHJoZrPyuXz0XzgQFIXLmffUSYi/filj3SUqQjKTC8uRaFtb/WG6et13gwAAGBtgjIAAAAAAI/R4M7BqnpoqvagTEREviLFshGjlyLqCMpUjF6KiJho6ImIijlOgjIAAMAGE5QBAAAAAHiMDnUdiqaGpqS+NHGprvOa+8opluIGjF6KSB+UGRmJKJXKdU97vur3uwuliM6KrjKCMgAAwAYTlAEAAAAAeIyaGpqir6svqesZvRQRkT9SPmt5YiKWJyfrOu9+0gZlZmYiKj+/q7M6KDM2vRjRtb/8YEpQBgAA2FiCMgAAAAAAj9nAzoFkXffopb6+qrpw+Upd591P2qBMRPX4pb1drVW/3ZpeqA7KTI/WeTMAAIC1CcoAAAAAADxmlUGZK1NXorhSrPmsfF91iqUwMlLzWQ+ynqBM5ef3dLVU/XZzajFiR2VQ5kadNwMAAFiboAwAAAAAwGNWGZRZKi3Flenau8A0HzoUkcsldeHy4w3KVHaUaW1ujO725qS+11HmQHnDzK2I5dpDQgAAAA8jKAMAAAAA8JgN7hysqocmax+/1NDSEk37n0rq4sjlNXbXprc3or093d7Lqz6/r2L80q2pxYiupyp+Ld0LywAAAGwQQRkAAAAAgMessqNMRH1BmYiIfF9fsi6sTqpkIJdL31Vm9ef37iiPX7o5tVA9eikiYvp6nbcDAAB4MEEZAAAAAIDHrL25Pfa27U3qy1P1hVvyRyqCMiPZj16KqD0os29HuaPMzemFiK5VQZmp0TpvBgAA8GCCMgAAAAAAm8DhHYeT9eXpeoMy5RTL8vh4LE9P13Xe/dQelKnsKLMYpdVBmekbdd4MAADgwQRlAAAAAAA2gb4d5S4wV6au1HVWvq86xbIR45cqpjutaXQ0olgs15UdZQpLKzFZ6ohoLIdnYlpHGQAAYOMIygAAAAAAbAKHu8odZW7N34q54lzNZ+VXpViKGxCUSdtRZmXlXljmQ3u7Wqt+vzldiOjaV34wO5bB7QAAAO5PUAYAAAAAYBM40lWdPLkyXXtXmebDh6vqwshIzWc9SNqgTERE5ecrRy9FRNycWoho2Vl+sJj9mCgAAIAPCcoAAAAAAGwCR3ZkF5RpaG2NpqeeSurCSPYdZVZlcdZ040Z5vXfHqo4yUwsRLV3lB4IyAADABhKUAQAAAADYBFZ3lLk8XV+4JV/R8qWwAaOXDhxIv7cyKLOns7qjzK3pxVVBmZk6bwYAAPBggjIAAAAAAJtAe3N77G7bndSXp+oMyvT1JevC5exHL7W1RXR1PXxfRMTNm+V1vqkhdnXky7/pKAMAADxCgjIAAAAAAJtEZVeZujvK9JXPWh67Hcszs3Wddz8V053WVBmUiYjYXdFV5s5sQVAGAAB4ZARlAAAAAAA2ib4d5S4wF+5eiFKpVPNZzUeqRzkVr2Q/fmnfvnT7VgdldnWWO8rcnl6MaOks/ygoAwAAbCBBGQAAAACATeJk78lkfXfxblyfvV7zWZWjlyIiCiOPLyhz40Z1vesjHWV2lH9cnIqoIyAEAACwFkEZAAAAAIBN4tndz1bVZ+6cqfms/OHDVXVhZKTmsx6k1o4yuys6ytyZWawevRSliEL2Y6IAAAAiBGUAAAAAADaNEz0nojHXmNTv3Xmv5rMa2tujae/epC5czj4o89RT6fbdvFndJGZ3RUeZu3PFWG7urH7B+CUAAGCDCMoAAAAAAGwSrU2tcbT7aFK/d7v2oExERP7IkWRdfIyjlwqFiMnJcr2rI1/1+0y0Vb8gKAMAAGwQQRkAAAAAgE3k2V3l8Utnxs9EqbIVyzo195WDMo9z9FJExI0b5fWuio4yERGTK63VmwVlAACADSIoAwAAAACwiXxs18eS9eTiZFyduVrzWfnDh5P10thYlIrFuu62WtrRSxH3xi99aFdndUeZ8aXq4EwsTtVxKwAAgAcTlAEAAAAA2ESe3/18Vf2DWz+o+aymPXur6qU7d2o+637W01GmMiizu6M6GHO7uDooo6MMAACwMQRlAAAAAAA2kad7n462prakri8os7uqXhq7XfNZ91NzUKaruqPMrcXqWlAGAADYKIIyAAAAAACbSHNDc1VXmTdvvVnzWU27VwVlbo/VfNb9tLVFdHWl23vjRnndnm+KtubGpL5ZaK7eLCgDAABsEEEZAAAAAIBN5uN7P56sL05cjMnFyZrOadqzp6peGss2KBMR8dRT6fZVBmUiInZ1lrvI3JhriIhc+ceCoAwAALAxBGUAAAAAADaZ03tPJ+tSlOKtsbdqOqextzeiofxn4KXb2Y5eikgflBkerq53dbYk67HZpYiWitY0OsoAAAAbRFAGAAAAAGCTObXnVOQqOqy8PfZ2TefkGhvvhWV+aHkDgjJHjqTbNzRUXe/uKHeUuTOzKCgDAAA8EoIyAAAAAACbTFe+K/p29CX1hYkLNZ9VOX5pI0YvDQyk23f5csTSUrneXdFR5vZMQVAGAAB4JARlAAAAAAA2oWPdx5L1xYmLNZ/TtHt3sl4ay76jTNqgzPJyxNWr5bq3s9xRZnxWUAYAAHg0BGUAAAAAADahwe7BZH1l+koUlgs1nVPVUWYDRi+lDcpEVI9f6m0vB2Xmi8uxnO8s/ygoAwAAbBBBGQAAAACATaiyo8xyaTmGJofW2P1g1R1lxqJUKtV9t0q1BmW625urfis0dpSLxak6bwUAAHB/gjIAAAAAAJvQ0e6jVXWt45cqgzKlxcVYmZmp616rHToU0diYbm9VR5mOfNVvCw2VQRkdZQAAgI0hKAMAAAAAsAn17+iPxlw5gXJh4kJN5zTt3VNVL41lO36pqSniyJF0eyuDMj2rgjJzufZyISgDAABsEEEZAAAAAIBNKN+YjyM7ygmUS5OXajqnsqNMxL3xS1lLO36pqqNMe3VQZrbUWi4EZQAAgA0iKAMAAAAAsEkd6z6WrGsevbR3b1VdHB2t6073MziYbl9VR5lVQZmpUlu5WC5ELC1mcDMAAIBqgjIAAAAAAJvUka5yR5nrs9ejVCqt+4zmAwfuzUf6ocLIcBZXq5K2o8z16xGLP8y/dLU2RWNDLvltYqWlerOuMgAAwAYQlAEAAAAA2KQOdB5I1ovLi3Fn4c66z8g1NUX+8OGkLgwNZ3G1Kv396fdeu3bv34aGXPS0NyfP7y61Vm9cnKr/YgAAAKsIygAAAAAAbFIHOw9W1ddmrtV0Tr4iyVIYHq7jRvd35MjD93zo8uXyunL80p2ijjIAAMDGE5QBAAAAANik9nfur6pHZ0ZrOidfMRupMDISpZWVuu61WhZBmbFivnrj4kydtwIAAPgoQRkAAAAAgE3qQMeBqrrmoEx/X7IuLSzE0s2bdd1rtQMHIhpS/rV5ZKS87ukoj166sdBcvVFHGQAAYAMIygAAAAAAbFKtTa2xq3VXUtcelOmvqgtDQ/Vc6yOamiIOHnz4vojqjjK9HeUuMtcFZQAAgEdAUAYAAAAAYBM70FnuKnNt9lpNZ6wOyiwOD9dxo/tLO37pQaOXrs03VW9cnMrgVgAAANUEZQAAAAAANrHKoEytHWWa9uyJhvb2pC5swqDMnWK+eqOOMgAAwAYQlAEAAAAA2MQqgzLXZ65HqVRa9xm5XK6qq0xhZCSLq1VZT1Dmw/+EnorRS0vRFCtNbeWNgjIAAMAGEJQBAAAAANjEDnSUgzILywsxvjBe0znNFUmW4uUrdd9rtbRBmbm5iPEf/if0djRX/bbc3FkuBGUAAIANICgDAAAAALCJVXaUiYi4Ml1byCV/+HCyLl69GqXl5brutVraoExEefxS5eiliIhiU0e5EJQBAAA2gKAMAAAAAMAm1r+jv6oemhyq6ZzmI+WgTKlYjKWbN+u51kesJygzPHzv392dLVXPFxsqgzJT9V8KAABgFUEZAAAAAIBN7GDnwcg3lDuvXJq8VNM5+cPVSZZCxuOX1hOUuXjx3r97uqqDMnO5tnKhowwAALABBGUAAAAAADaxxobG6N/Zn9Q1B2UqOspERBSuXK7nWh/R3R3R25tu74UL9/5tbW6Mzpam5PlMtJc3CcoAAAAbQFAGAAAAAGCTG9w5mKwvTdQWlGnaty9yzc1JXcy4o0xExLFj6fadP19eV3aVmSq1ln8QlAEAADaAoAwAAAAAwCY32F0OylybuRYLSwvrPiPX2BjNhw4ldeHK4wvKfNhRJiJid2d5rNTEsqAMAACwsQRlAAAAAAA2ucqOMqUoxfDUcE3nNFeMXypeznb0UkTE8ePp9l25ErHww6zP7s5yR5k7xfI6CjMZ3gwAAOAeQRkAAAAAgE2uMigTUfv4pfzhI8m6cOVKlEqluu61WtqOMqVSxNDQvXVlUGasWO4uE4WZiJXlDG8HAAAgKAMAAAAAsOn17eiLhlz5z7kXJi6ssfvB8n19yXplejqW79yp+26V0gZlIsrjl/Z0lYMytxebqzfpKgMAAGRMUAYAAAAAYJPLN+ajb0c55HJ+4nxt5wwOVNWLF2vrTPMgtQRlKjvKTJXaqjctTmdwKwAAgDJBGQAAAACAJ8CJnhPJ+oPxD2o6o+Xo0aq6cOliXXdabdeuiJ070+0tB2XK45ZmQlAGAADYWIIyAAAAAABPgBO95aDM9dnrMbk4ue4zmvbti4aOjqTOuqNMLpe+q8z9Ri8JygAAABtNUAYAAAAA4AlQ2VEmIuLc3XPrPiOXy0V+cDCps/iW19MAACAASURBVO4oE7H+oEzl6KXp1aOXFtYfBgIAAFiLoAwAAAAAwBOgsqNMRG1BmYiIloqgTNYdZSLSB2WGhyMKheqOMrdLq+Y2zdzM7mIAAAAhKAMAAAAA8ETY07Ynelp6kvr98fdrOid/9GiyXrp5M5ZnZuq+W6Xjx9PtW1m5F5ZpbW6MrpamiIi4HTujFLnypukbmd4NAABAUAYAAAAA4AmQy+Wqusp8MP5BTee0HB2sqguXsu0qk7ajTETF+KUfdpVZiqaYaazoKiMoAwAAZExQBgAAAADgCXGy92SyPn/3fCwsLaz7jPxgdVBm4f3aOtM8SE1Bmc588my8obe8YUZQBgAAyJagDAAAAADAE+LUnlPJeqm0FO/deW/dZ+T7+qKhszOp599+O5O7fWjv3oiK49f0YVBmzw87ykRE3Cp1lzdM38zwZgAAAIIyAAAAAABPjBf2vFBVvzX21rrPyDU0RNupcuBm4a31n7Hm+bn0XWXKHWXKQZnryxWjl3SUAQAAMiYoAwAAAADwhNjbvjcOdBxI6rdu1RZyaft4OXCzeOFiLE9P1323SvUEZa4Ud5Q3TN+IKJUyvBkAALDdCcoAAAAAADxBXthbDrn8YOwHUaohSNL2QkVnmlIpFt55J4urJdIGZYaGIorF6tFLNytHLy0XIubvZno3AABgexOUAQAAAAB4glSOXxpfGI+rM1fXfUZrxeiliIj5jMcvHT+ebt/SUsTwcHVHmZulnupNMzezuxgAALDtCcoAAAAAADxBKoMyERFn75xd9xlNPT2R7+tL6rk3v1/3vSqlDcpERJw/H7G7M5/UY5UdZSIipq9ndCsAAABBGQAAAACAJ8qx7mPRkCv/afeDux/UdE7biy8m6/k33ojS0lLdd/vQ00+n33vuXPXopVuxqqPMtI4yAABAdgRlAAAAAACeIK1NrdG/oz+pz42fq+mc9pdfStYrc3OxcOZMvVdL7N0b0dWVbu+5c9Wjl8ZKO6s3zNzI7F4AAACCMgAAAAAAT5ine8otW87drS0o0/HSS1X13Guv1XWnSrlc+q4y585FtDY3RldLU0RELEY+5hp3lDfoKAMAAGRIUAYAAAAA4AlzovdEsh6dHY2pwtS6z2g+eDCaDx5M6tkMgzIR6wvKRETsrhi/NNVYMX5pdizDWwEAANudoAwAAAAAwBOmsqNMRB3jlyq6ysy/8WaUlpfruleltEGZK1ci5ucj9lSMX5qKjvKGxfWHgAAAAB5EUAYAAAAA4AlzoudEVf3B3Q9qOqft9CeS9crsbBSvX6/rXpWOH0+/98KFiN1d+aSeWGkv/7gwmdmdAAAABGUAAAAAAJ4we9v3RndLd1JfmLhQ0zktR49W1YWhobruVSltR5mIiPffj9hd0VHmznJr+UdBGQAAIEOCMgAAAAAAT5hcLheDOweTemRqpKZz8gMDVXXh0qW67lVpPR1lzp6tHr10e6mt/KOgDAAAkCFBGQAAAACAJ1Dfjr5kPTw5XNMZjT090bhzZ1IvZthRprs74qmn0u09cyZid1c5KDNZMnoJAADYGIIyAAAAAABPoP6d/cl6bH4sZgoz6z4jl8tVdZUpXMouKBMR8bGPpdt35kz16KWpyqBMcS5iqZDpvQAAgO1LUAYAAAAA4AlU2VEmImJkusbxS4PlEU6FDDvKRKQPypw7F9HTVhGUiY7qDYtTGd4KAADYzgRlAAAAAACeQAM7BqrqWscv5Qf6k/XS2Fgsz6y/M82DpA3KLC5GzN95QEeZCOOXAACAzAjKAAAAAAA8gQ51HYqGXPlPvMNTwzWd01LRUSYi264yaYMyERE3RvLJ+iMdZRYmMroRAACw3QnKAAAAAAA8gfKN+TjYeTCpRyZrHL00UN2ZpnDpUl33qvTss+n3XjjXGF2tTRGhowwAALBxBGUAAAAAAJ5QfTv6knWtHWXyhw9HNDUl9WKGHWV2747Ysyfd3jNnIvZ03hu/NBWCMgAAwMYQlAEAAAAAeEL17+hP1pcmL0VxubjuM3LNzZE/dCipC5eyC8pEpB+/dO5cxO4PgzKl1aOXBGUAAIBsCMoAAAAAADyhnt1dnm20uLwY5+6eq+mc/OBgsi5k2FEmIuLEiXT7zp+P2NN1LygzraMMAACwQQRlAAAAAACeUC/seaGqfmvsrZrOyQ/0J+vCyEiUlpfruFW148fT7bt7N6J9pS0iIorRFPPRUv5RUAYAAMiIoAwAAAAAwBPqUOeh6G3tTepagzItFR1lSoVCFEdH677bh9IGZSIilic6k/VkqaKrjKAMAACQEUEZAAAAAIAnVC6Xi1N7TiX122Nv13ROfmCgqs5y/NJ6gjKLd9qS9ZSgDAAAsAEEZQAAAAAAnmCV45euzlyNO/N31n3G6qDM4qVLdd/rQ4ODEblcur2TN1uT9VR0lH8QlAEAADIiKAMAAAAA8ASrDMpERLx56811n9HU0xON3d1JXRgarvdaidbWiCNH0u29PZpP1jrKAAAAG0FQBgAAAADgCfb87ucj31AOmHxv9Hs1nZMfHEzWhQw7ykSkH790/XJTsp4KQRkAACB7gjIAAAAAAE+w1qbW+MS+TyR1zUGZgf5kvTg8VOetqqUNygxfykVTw705TVMlo5cAAIDsCcoAAAAAADzhXtn/SrK+OnM1rkxfWfcZLQMDyXp57HYsT09ncreI9EGZqalc7GrqurfWUQYAANgAgjIAAAAAAE+4Vw68UlXX0lWmcvRSRERhKLuuMmmDMhERnfM9ERExVaoIyhTnIpYKmd0HAADYvgRlAAAAAACecCd7T0Z3S3dSf//W99d9Rr5/oKpevHSp7nt9aD1BmaaZHRERMRUd1T8sTmV2HwAAYPsSlAEAAAAAeMI15Bri2d3PJvUHdz9Y9xn5w4cimpqSujA0nMXVIiJiYCCiIeVfo5cm7gVkqjrKRBi/BAAAZEJQBgAAAABgCzjRcyJZD00MRWF5faOKcs3NkT98OKkLGXaUyecj+vvT7Z0Za42I+3SUWZjI7D4AAMD2JSgDAAAAALAFVAZllkpLcWly/UGX/OBgsi4MD2Vyrw+lHb80PpqPCB1lAACAjSEoAwAAAACwBZzoPVFVfzC+/vFLLQP9ybowPBKlpaU6b1WWNigzOtIUpVLEVAjKAAAA2ROUAQAAAADYAvp29EW+IZ/UH9xdf1Amf/RYsi4Vi7F4MbvxS2mDMnNzuViebYmp0urRS4IyAABA/QRlAAAAAAC2gKaGpjjWUw66nBs/t+4z2p5/rqqef/utuu/1oaefTr+3ZXZHTOsoAwAAbABBGQAAAACALeJET3n80tnxs7FSWlnX+/nBwWjoKHdyWXj7nczulrajTERE+8LOKEZTzJVayg8FZQAAgAwIygAAAAAAbBHP7S53hJkqTMXQ5NC63s81NETr888n9fw72QVl+voimprS7W1e6IyIiKnKrjKCMgAAQAYEZQAAAAAAtojTe09X1W/cfGPdZ7SdOpWsF8+di5W5ubrvFXEvJHPgQLq9KzNtERExVRKUAQAAsiUoAwAAAACwRQx2D8bOlp1JXVtQptxRJlZWYuHMmSyuFhERR46k21eYaI2IiKkoj4ESlAEAALIgKAMAAAAAsEU05BriE3s/kdRv3npz3We0Pn+qql44c7bue33o8OF0+6bvNEeEjjIAAED2BGUAAAAAALaQT+77ZLK+MXsjRmdG1/V+09490dDVldSFkZHM7pa2o8ydm01RKkVMhaAMAACQLUEZAAAAAIAt5PTe01X127ffXtf7uVwu8v39SZ1lUCZtR5n5uVysLDTHVMnoJQAAIFuCMgAAAAAAW8jTvU9HY64xqc+Nn1v3Gfm+vmRdGB7O4loRkb6jTETEynSrjjIAAEDmBGUAAAAAALaQlsaWGNg5kNQf3P1g3WdUdpQpjo7GSqGQxdVSd5SJiOha2hlTpYqgTHEuYimbewAAANuXoAwAAAAAwBbzdM/TyfqD8RqCMhUdZWJlJYpXrmRxrXV1lGktdMVUdFQ/XJzK5B4AAMD2JSgDAAAAALDFnOg9kaxvzt2MycX1jS2q7CgTEVEYGcniWtHTE9He/vB9ERGNcx3VHWUijF8CAADqJigDAAAAALDFnOg5UVWvt6tMvr+vqi4MZxOUyeXSd5VZmmr9aEeZhYlM7gEAAGxfgjIAAAAAAFtMZUeZiIgP7q4vKNPY1RWNvb1JXRgezuJaERFx+HC6fROjbTrKAAAAmROUAQAAAADYYna37Y7e1nLQZb0dZSKqxy9lNXopImJwMN2+68PNcXt5R/XDqdHM7gEAAGxPgjIAAAAAAFtQ5filc3fPrfv9fF95/FKWHWWefz7dvmIxF1fGj0Sh1Fh+eOdiZvcAAAC2J0EZAAAAAIAtqHL80oWJC1FcKa7r/cqOMks3b8bK3Fwm9zp1Kv3ehbHuuFzaV35w50ImdwAAALYvQRkAAAAAgC3o6Z6nk3VxpRhDk0Prer+yo0xEROHy5Uzu9dxz67jDVE8MlfaXH4xfyuQOAADA9iUoAwAAAACwBVV2lImI+GD8g3W9nx/or6oLwyN13uienp6Iw4fT7S3d2RmXSk+VH9y5GLGyksk9AACA7UlQBgAAAABgCxrYORDNDc1Jfe7uuXW9nz9ypKouDA9nca2IiHj++XT7Zm90VneUWZqPmB7N7B4AAMD2IygDAAAAALAFNTc0x9Huo0m93o4yDW1t0fRUuZtLYSSbjjIR6YMyU2P5OLdQHdiJOxcyuwcAALD9CMoAAAAAAGxRT/c8naw/uLu+oExERL6vL1ln2VHm1Kn0e89NDFY/EJQBAADqICgDAAAAALBFneg5kazHF8bj9vztdb2f7+9P1ll2lDl+PP3eW5P7YrbUUn5w51Jm9wAAALYfQRkAAAAAgC3qRO+Jqnq945cqO8osj4/H8sREJvcaGEi/d2miIy6X9pUfTF7J5A4AAMD2JCgDAAAAALBFVXaUiVj/+KWW48eq6oX336/7ThERu3ZFdHam21uabo+JUsXmhclM7gAAAGxPgjIAAAAAAFtUd2t37G3fm9Tr7SjT+swzVfXCe2cyuVcul76rTNNsV0xGR8UlBGUAAIDaCcoAAAAAAGxhlV1lzt09t653m3bvjqZ95bFHC2eyCcpEpA/KLE+2x1SpvfxgIZvxTwAAwPYkKAMAAAAAsIWd6C0HZYYmh2JxeXFd77d+7GPJ+nEEZWZvt8RESUcZAAAgG4IyAAAAAABbWGVHmeXSclycuLiu9yuDMoXh4Viemc3kXoOD6fYVFhrjxlx5fFRpYTJiZSWTOwAAANuPoAwAAAAAwBZ2svdkVf36jdfX9X7rs+WgTJRKsfjB+1lcK3VHmYiIyxOHk3WutBJRmMnkDgAAwPYjKAMAAAAAsIX17eiLfe37kvovrv7Fut6v7CgTEbFw9tEHZUYnDlQ/WJjI5A4AAMD2IygDAAAAALCF5XK5+NFDP5rUb9x8I2bW0ZGlad++yLW1JXXx2rVM7tXfn37vrYl91Q8WJjO5AwAAsP0IygAAAAAAbHGfO/S5ZL1UWorvXf9e6ndzuVw0Hyx3dCmOjmZyp87OiD170u2dnt5b/WBeRxkAAKA2gjIAAAAAAFvcy/tfjpbGlqRe7/il5gMVQZmMOspEpB+/ND+zq/qBjjIAAECNBGUAAAAAALa4tqa2+ORTn0zqV6+/GqVSKfX7VUGZjDrKRKQPysxM7Kx+sKCjDAAAUBtBGQAAAACAbeDTT306WV+fvR5Xp6+mfrf54MFkvTw+Hitzc5ncKW1QZupOW6yUcuUHOsoAAAA1EpQBAAAAANgGXt7/clX96o1XU79b2VEmIqJ4/Xomd0oblFleaojhqUPl78+MZ/J9AABg+xGUAQAAAADYBk72nowd+R1J/dr111K/m6/oKBOR3filtEGZiIj37p5I1rNTgjIAAEBtBGUAAAAAALaBhlxDvPxUuavMqzdejVKplOrdptUdZa5dy+RO6wnKvH/3eLJe1FEGAACokaAMAAAAAMA28an9n0rW4wvjcWHiQqr3mnbvjlw+n9TFa9l0lDlyJKIh5V+pL0yUUzXLs3cz+T4AALD9CMoAAAAAAGwTL+9/uap+7Ua68Uu5hoZo3r8/qbPqKJPPRxw6lG7vyMSRZF1amMzk+wAAwPYjKAMAAAAAsE0M7BiIvW17k/qvrv9V6nebDx5M1oVrV7O7U8rxS5duH0vWjYuCMgAAQG0EZQAAAAAAtolcLlfVVeb1G6/H0spSqnfz/X3JujA0HKVSKZM7DQ6m2zd0cyAWl+6Nf8ovTWfybQAAYPsRlAEAAAAA2EZefqoclJkpzsTZO2dTvZcfKCdaVqamYvnOnUzu8/GPp9u3tNwUb918LiIi2pYFZQAAgNoIygAAAAAAbCOf2v+pqvr7t76f6r38YPWMpMVLlzK5z0svpd/719dOR0REWyxGaTaboA4AALC9CMoAAAAAAGwj+zv2x962vUn9zu13Ur3XsmpGUuHSUCb3+fjHI5qa0u3969HTyXri8tuZfB8AANheBGUAAAAAALaRXC4Xz+95PqnfHksXOGnaty9y7e1JXRjKpqNMW1vEc8+l21sZlBkfEpQBAADWT1AGAAAAAGCbeX53OSgzOjsat+dvP/SdXEND5Pv7knoxo44yEenHL50dOxGzhXthneL19zL7PgAAsH0IygAAAAAAbDOn9pyqqtN2lWkZKI9fKlzKpqNMRPqgTCka4oM7x+/d5e65zL4PAABsH4IyAAAAAADbzLO7no2GXPnPw+/cfifVe/nBgWRdHB2Nlfn5TO7z4ovp9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8Ybb2jMmDHJvgP1799fAwcO1J49ezR79uzMekvZXtLf8BigAwAAAE8i2+YQAwAAT6V58+aZbsb+3//9n2bMmJFiuF2ScufOLX9/f3388cfJXvP29taaNWvUunXrVG9G58iRQ506ddLQoUONdXv37k1zms2kqlSpopkzZ3JjDgCykWnTpsnR0VFjxozR2rVrNWjQIPn7+8vf31+jRo3Shg0b5OPjY9RfvXq1zp07Z3VbKQViNm7cqJEjR6pDhw5q3769Ro4cqY0bN6ply5ZGvYRATOLRwhL4+PiofPnyRnnlypU2v78VK1YYywmjjwHAsyA+Pl5vv/22hgwZkiyMIEnFixfXvHnzTKPkzp4923QMT3DlyhV99dVXRvmll17SwoULrYbRpAcjVX733XcqUqSIpAfhv9RGC7Zm2rRpaYbbJWnPnj3Gsr+/f6rhdulBCL5x48b68MMPrb4eFxenCRMmGGVHR0fNmzcvWbg9wSuvvKKvv/7aNLreZ599pvj4+DT7Lj34c3rrrbesBjsS9j927Fh5eXkZ61avXm3TtgE8eSwWi6ZOnZos3J6gTZs2atiwoVHeu3evjh07ppdeekkzZ85MFm6XpLx582r8+PGmdQkjCye1YsUKnTp1yij7+vrqs88+SxZulx7cb5o8ebKpP8ePH0/XtbqtIiIiNHPmTKOcN29efffdd8nC7QlatWqliRMnGuX79++byrbo0qWLBgwYkGq4XXrw3STxuah27dr67rvvUh0t2NnZWS1btrT64HDTpk2VP39+o5z4gavU/PLLL8ZyxYoVTd+fEhszZowxcrQkjR07Vv37908xxCpJhQoV0sCBA/X222/b1JeUZMX1BJAVMvM6PMH8+fM1ePDgNGdUrVSpkr7//nu5ublJevDvJiGknZKsOC6cOnVKbdu2NcLtTk5O+uKLL1INt0sPviMkvs7+6quvUrzPU6lSJc2bN0+Ojo42X5tbY+tvGjt37tS2bdtsaufk5KQhQ4aoXbt2xrorV66kOrvi4xYfH69+/fqpT58+Vv8eu7q6aurUqaZrhNS+p2zbts10vqxbt64mTpxo9TuQp6en5syZozJlymToz+5JEh0drYCAANO6KlWqZFFvAAAAgIdHwB0AgGdUVFSUaepNLy8vjR49OsWpOm3h5OSUrvYdOnQwBSsSj5xlixEjRli9GQoAyDrx8fEaN26c/P39rU4j7OnpqalTp5rOFyn9YPUoAzF+fn7G8unTp41R/FITFhamP/74wyi3bNmS8xCAZ4a7u7sGDx6cah1PT08NGDDAKN+9e9fqcfj77783Rkl3cHDQpEmT0gz8ubu766OPPjLKe/bsMQIsaWnUqFGaQfUE169fN5aff/55m9qkZsuWLfrnn3+M8rvvvqsKFSqk2qZq1apq3769UQ4ODtaWLVts2l/u3LmtPoycmLOzs958802jfPz48WTT1wN4OtSrVy/NGSD+7//+L9m64cOHm0ZNT6pSpUqmUOSRI0es1vvhhx+M5Vy5cmnUqFFWvyMksLe315gxY+Ti4mJ1G5ll+fLlpvBlv379TPenrHn99ddN55IDBw7or7/+sml/+fLl07///e8061ksFtNIwrlz59bEiRPl7Oxs036scXR0NAVGN23apLCwsFTb/Pnnn/r777+Nsr+/v9V6hw4d0v79+42yr6+v2rRp89B9Ta/HfT0BZJXMvA5PkPg4m5aiRYuqQ4cORjm1e/hZcVzYsWOHOnToYFzHu7m56dtvv9Xrr7+earsLFy5o7969pr7Wq1cv1TY+Pj6mz+Jh2PqbRuLzn52dnT799NM0j3GDBg1SgQIFjPLSpUuN2Uey2nPPPZfmrLuenp5q2rSpUU7p+kKSfv75Z2PZwcFBo0aNSvW3qVy5cmn48OHp6HHGDRs2TD4+Pjb9lzDbS2aIjY3V8OHDdePGDWNdgQIFVK1atUzbBwAAAPC4EHAHAOAZtWvXLtP0jR07drQ6usWjlCNHDtWqVcsoHz161Oa2ZcuWVcWKFR9FtwAAGVCzZk1TaM6aMmXK6OWXXzbKWRGISRpOTzwye0p+/fVXI0AhPRhNHgCeFc2aNZO7u3ua9Vq0aGGazWnTpk3J6qxZs8ZYbtKkiZ577jmb+uDr62v6zrJz506b2qXneJ34HHLo0CGb26UkcTDdzs7OFFxPzTvvvJPidlLTvHnzVGfTSpB49L67d+8qODjYpu0DeLLYcvxLem+lVKlSqlSpUrranT17NtnrwcHBCgwMNMrNmjUzjSSeksKFC5vCbadOnTI9KJQZNm/ebCznypXL9PBrat59990Ut5OaFi1a2BQm/euvv0yfpZ+fnwoVKmTTPlLzr3/9ywj9xcbGpvndZ+nSpcayq6urWrRoYbXeunXrTOUePXpksKfp87ivJ4CskpnX4Q+rbt26xvLZs2d1584dq/Ue93FhxYoV6tmzpyIjIyVJRYoU0U8//aQaNWqk2faPP/6QxWIxyraOHv+vf/3r4Tor23/TiImJ0Y4dO4xy9erVVbZs2TTbubq66q233jLKN27cSNdvLo+Sn5+fTYMjJf6ecuHCBdN9uARJP59atWrZdA6oXbu2SpYsaVuHnzAxMTEKDg7WypUr9fbbb2vt2rWm1/v375/mAxIAAABAdkTAHQCAZ9S+fftMZV9f3yzpR+IRRUJCQmxulzgYCQDIPmz9QTDxD1bWRsx71IGYfPnymaadXrNmjdUfzRJLPPpZ2bJlVb58+TT7AwBPi/r169tUz9nZ2RQoOXHihGka+HPnzplGkrN1ZHXpwSi0iQMhqY3ol1h6vjsk3v7q1as1ffr0FAM8tkgcki9TpoyKFStmU7uSJUvK29vbKB8+fNimdrZOO580MBkREWFTOwBPFluC6kmvsStXrmzTthO3s3YMSXrcSjzTUlpeffVVUzkzHjhKEBcXp+PHjxvlmjVr2jzgQ61atUx1bT02V61a1aZ6j+penZeXl+k8njjAntTt27e1YcMGo9yqVasUP5/EozQXK1YszRlKMlNWXE8AWSWzrsMzIvE9/Pj4eF27ds1qvcd5XJg9e7aGDh1q3Mvx8fHRkiVLVKZMGZvaJw5+58iRw6ZQvCR5e3ubPo/0sPV7ycmTJ00jr6fnGNe4cWNTOTPPoRnxMN9TLBaL1e9ip06dMt3DS2vk/cTSUzejPD09VaJECZv+c3Nzs3m71kaGf/HFF/Xqq69qyJAhyWaY6dKli833awEAAIDsJuU5JgEAwFMt8VTD+fPnV9GiRTN1+ydPnlRAQICOHz+uc+fOKSIiQlFRUYqLi0uxTXpCFbaOygQAeLwe5gerRxGI+fXXX43yoUOHVKRIkWT1/Pz8jBHNbt68qd9//11NmjSxus3AwECdPHnSKDN6O4BnjY+PT7rqJow4HhkZqatXrxrfN06dOmWqW7p06XT1w9PT01i2ZURfV1dXU5u0dOjQQbt37zbKs2bN0vz581WnTh3VqlVLL7/8snx8fJQjR440t2WxWHThwgWjnJ7PUJLKlStnPAR2/vx5WSyWVGcykWRz2CZpWDEqKipdfQPwZLDlmJD0eGDLQ6VJ21k7hpw7d85UfuGFF2zarvTg+JfatjLi6tWrpv6m59hsb28vHx8fIyxoa79svYeV+F6dvb19ps5c2K5dO23btk3Sg3PK3r17VbNmzWT1Vq9erejoaKPctm3bFLd55swZY9mWhyky0+O+ngCyUmZdh1sTFhamgIAA7d+/X3///beuX7+uyMhIU7jamtu3b1td/ziOC3FxcRo1apSWLFlirKtVq5ZmzZpl00xGCS5dumQsFytWzKaZNhKULl1a169ft7l+AlvPBxk5h5YtW1b29vbGww2ZeQ7NiIx8T/Hw8DCtSzpzTHr/jTwuH330UZbePyxWrJgGDRqkZs2aZVkfAAAAgIwi4A4AwDMqPDzcWH7YEUesCQoK0ujRo5ONOmWLe/fu2Vw3PTerAQCPT8GCBW2qlx0CMQ0aNFC+fPkUFhYmSVq1alWKAffEo7c7ODioVatWNvcHAJ4G+fLle+i6ERERRrDm5s2bptfeeuuth+7TrVu30qyT3u8NTZs21fvvv6958+YZ66Kjo7Vp0ybjoag8efKoWrVq8vX11WuvvSZXV1er27p9+7Zp1ExbQ6MJEofv4uLiFBkZmeb7cXZ2Ttc+ElgslodqByB7e5hjwsMeR5JKeoxOz8NGSY+XmTnLRNJtZeTYbGu/bD0XJb5Xlzt3buXMmTNdfUtN/fr15eXlpeDgYEkPRnG3FnD/5ZdfjOWKFSumOGvVnTt3TKPnZua9RVs87usJICtl1nV4Yvfu3dOMGTP0/fffpxlmt8baffzHdVw4ePCgDh48aJRfe+01TZ06VU5OTunaTuKQfnpGz36Y+glsPR9k5Bzq4uKi3LlzG+eo7DJTU2Z+T8nI55Oef0/ZlaenZ7LvoI6OjsqdO7c8PT31wgsvqEaNGqpVq1aaD0gDAAAA2R0BdwAAnlGRkZHGsq1TMafl6NGj6tq1q9Wbpg4ODsqbN6+cnJzk4PC/S5Bbt2491A9JtoyYCAB4/J6kQIyjo6NatmyphQsXSpK2bt2q8PBw5c2b11QvLi7ONCJ8vXr10h2EAYAnXXpGNEwayEv83SOl0R4fRuIRZlOS+LuHrQYNGqTq1atrxowZOn78eLLXb9++ra1bt2rr1q2aMGGC+vTpo44dOyarl/QBrvR8hpL1z5EHfQE8KZIeA9MT1k7tPJJRSbeVkWOzrf2y9R7Wo7hXl8De3l7/+te/NG3aNEnSxo0bk333OXr0qGlkdH9/f5v6+ij6m5bHfT0BZKXMug5PcO/ePXXv3l179uyxug13d3e5uLiY7u/ExcUZD8hI1kPHj+u44OzsbArYnzp1StevX5eXl1e6tpM42J/ecHx66yew9XyQkXNoQv2Ee2GZeQ7NLjLyPetxn68ehaweGR4AAAB4nAi4AwDwjEo8wkNmTEcfExOjQYMGmUKEdevW1VtvvaUqVaqoUKFCsre3T9Zu+vTpmjVrVob3DwB4ujyuQEzr1q2NgHtsbKzWrl2rDh06mOr88ccfpqmn/fz8bO4LADwt7t69m+JI5UklDYolbpc0fODl5fXQD68WLlz4odrZomHDhmrYsKGCgoK0Y8cOHTx4UH/++afpfCA9GG133LhxOn36tMaOHWt6LWl44u7du+nqQ2qfIwBkd0mPgdHR0TY/pPMoj39Jt5WRY3NmH5cz+15dUm+//bZmzpyp2NhYxcTEaOXKlerUqZPx+tKlS019adGihU19fVT9Tc2Tcj0BZIbMug5P8PXXX5vC7UWLFtU777yjV155RaVKlbI6cMGlS5dSnPEupX09quNC5cqVVadOHeOBnUuXLumdd97RggUL9Nxzz9m8ncTnpPSGwO/cuZOu+ull7RyaHo/yXJUdZOR71uM+XwEAAADIGALuAAA8oxKP0JQ0pPEwNm/erPPnzxvl7t2768MPP0yzXXaZIhMAkL08rkBMuXLl5OPjo8DAQEnSihUrkgXcV6xYYSznzZtXjRo1sqkfAPA0CQsLszkcERYWZiq7ubkZy0lnyfj6669VtmzZjHfwEfH29pa3t7fee+89SdL58+e1fft2rVy5UidOnDDqLV26VE2bNlX9+vWNdXny5JG9vb3i4+MlSTdu3EjXvkNDQ43lHDlyPJXhFABPL3d3d1M5NDTU5uv5pMfLxOeRjEq6rYwcmzOzX5L5HHnnzh1FR0ene9Te1OTPn19NmjTR+vXrJUm//PKLEXCPjIzU2rVrjbqtWrVKdZTb3Llzy9HRUbGxsZIy595iejxp1xNARmTWdbj04MH+H374wSh7e3vr559/TvN4ZsusCY/zuNCzZ0+5uLjo888/lyRduXLFCLl7e3vbtA0PDw9j+erVq+naf3rrp5e1c6it7t27ZwrgZ/a5KjvMepGRzyfpvxEAAAAA2VvyYVQBAMAzIfGPPjdu3NCVK1cytL1du3YZy7lz51bfvn1tanf58uUM7RcA8HTKyI9V6Q3EJJ7W99ixYwoKCjLKt27d0pYtW4xyixYtHnoqagB4kiU8CGSL06dPG8uurq6mkVFLly5tqvuowyGZrWTJknr33Xe1fPlyDR482PTaypUrTWU7OzvTKJJ//fVXuvZ16tQp037t7OweoscAkDWef/55UznxMS0tSesm3VZGFC5c2BTcTs+xOT4+3nQ+zMx+SeZ7dfHx8Tp+/Himbl+S/P39jeUzZ87owIEDkqQ1a9aYRrVt27ZtmtsqU6aMsXz06NFM7GXanvTrCSA9Mus6XHpwzyPxgDO9e/e2KQB96dIlm/b/OI8LnTp10ujRo41r5GvXrqljx442H9fLlStnLIeHh9v8Hu/du6czZ86kv8PpkJFzaGBgoPGArbVtJUj8AJWtofWYmJhsMWBRqVKlTOXEf+/Tkp5/TwAAAACyHgF3AACeUdWrVzeVN27cmKHthYSEGMulSpWyKfwXExOjgwcPZmi/AICn0+MMxLRq1UoODv+b4CzxiO3r1q1TTEyMUfbz87O5HwDwNPnjjz9sqhe3qayzAAAgAElEQVQTE6N9+/YZ5QoVKsje/n+3IMuXL296iGnPnj2Z18nHrGvXrvLx8THK1oIuVapUMb1u6wO+Fy5cMD1w9dJLL2WgpwDw+CU9bv3+++82t926daupnPhYmljia/jEYb7U5MiRQxUrVjTK+/btsznYt3fvXlMIPLOPzTVq1DCVAwICMnX7klSrVi1TMHDp0qWm/0tSxYoVVb58+TS3lfje4uXLl3Xy5MlM7GnqnqbrCSAtmXUdLpnv4UvSCy+8YNO2Ew9uk5rHfVxo166dPvvsM+N9hoaG6r333rPpAaGk5xZbfx/ZsmWLMUr9o1K+fHnT7yvbtm2zuW3iQRqklM+hefLkMZaT/r1IybFjx3T//n2b+/KolCtXTo6OjkbZ1n8j6a0LAAAAIOsRcAcA4BlVp04d03S+CxcuNP1IlxH37t2zqd6yZcuyxYgfAIDs53EEYhJ4enqqXr16Rnn16tVGQCbxaLxlypTRiy++aHM/AOBpsmHDBpuu3deuXavbt28b5SZNmphet7e3V/PmzY3ysmXLdOfOnczr6GOWOCRoLezRuHFjY9liseinn36yabs//PBDitsBgCeBl5eXKTi5YcOGZDMtWRMSEqLffvvNKJcvX15FihSxWtfV1dVYTnzuSUviY2pUVJSWL19uU7tFixaluJ3M4OPjYxqZfMWKFbp27Vqm7kMyj84eEBCgvXv3msKgiUd5T02LFi1M5a+//jpzOmiDp+16AkhNZl2HW2PLffywsDCtXr06zXpS1hwXWrdurSlTphgPPYWHh6tTp046dOhQqu3q1asnDw8Po/z999+n+ftIfHy8/vOf/2S802lwcnIy3afav3+/TaOUR0VFadmyZUY5f/78qlSpktW63t7exnJan1WCxNvOSk5OTqpbt65R3r17t00j8O/Zs0fnz59/hD0DAAAAkNkIuAMA8IzKmTOn3nnnHaMcHBys0aNH2zziVVJFixY1lv/+++80byhevHhRU6dOfah9AQCefo8jEJNY69atTdvYvXu3zp07p8OHDxvr33zzTVu7DwBPnfDwcE2ZMiXVOmFhYZo2bZpRdnFxsXrs7N69uzHiXnh4uAYNGqS4uLh09ScmJuahv7ukJDo6WqGhoTbXt1gspinuvby8ktVp1KiR6bvSwoULdeLEiVS3e/jwYVMQ3svLS40aNbK5XwCQXXTo0MFYjoqK0tixY2WxWFKsHx8fr9GjR5tGVE987yqpxMfdCxcu2DzgQuvWrZUrVy6jPH36dF25ciXVNgEBAdq8ebNRrl69us0jH9vKzs5OXbt2Ncp37tzR4MGDTTNKZQY/Pz+5uLhIku7evauBAwcar7m6uiYLqKakcuXKplHnAwIC9Msvv2RqX1OTXa8ngMyWmdfhSa9Xk472nVR8fLyGDx9u88A4WXVcaNGihb766ivjmHD79m116dLFNKJ9Uk5OTqYHekJCQjRs2LBUjwmTJk1K1wyDGZH4HGqxWDRq1Kg0zwdTp041PRjVtm3bFGfaTTywRHBwcJojmx84cMA0CERWS/xnd//+fX366aep/tlFR0dr/Pjxj6NrAAAAADIRAXcAAJ5h77//vsqUKWOUV61apb59++rq1asptomKitLSpUv12WefmdYnHlEkPj5eAwcOVFhYmNVt7Nu3T+3bt9ft27eTTZMKAECCRx2ISaxhw4ammU1WrFhh+uEuR44ceuONN9LTfQB4qtjb22vJkiWaMmWK1ZHKL1++rPfff980vf0HH3wgd3f3ZHW9vLxMYbotW7aoY8eONo1KeObMGU2fPl2vvvpqpo/UGhoaqkaNGmnkyJE6cOBAquecmJgYjR8/XmfPnjXWvfbaa8nq5ciRQ0OGDDHKsbGx6tatW4phm927d6tHjx6mz3jYsGF8bwLwRPLz81P58uWN8oYNG1IMSkZGRmro0KGmsGXFihVTvQZPHM67e/eupkyZYvoukBI3Nzf16dPHKIeHh6tz586mh5YSW7dunQYNGmSUHRwcNHjw4DT38zDefPNN1a5d2yjv3r1bnTp1UlBQUIptYmJitG7dOpv75O7urtdff90oJ36QuFWrVqbwf1o++eQT5cyZ0yiPHDlSX331Vap/DiEhIZo2bZr++9//2rwfa7Lr9QSQ2TLzOrxChQqmUcvnzp2r7du3W91vaGio+vbtqy1btqTrWjSrjgtNmjTRrFmz5OzsLOnBfaRu3bppx44dKbbp2bOnSpQoYZQ3bNigd955R/v27TOFpY8dO6YPPvhA3333nezt7U2zOD0qderUUcOGDY3yoUOH1LdvX6u/ucTGxmry5MmmWaCKFi2qzp07p7h9X19f47OSHvw5XbhwwWrdzZs3q0ePHoqLi5Odnd1DvJvM17BhQ9WqVcsob9++XcOGDbN6jREWFqZevXrp9OnTfK8CAAAAnjAOWd0BAACQdXLmzKmvvvpKHTt2NEYq3LRpk/744w+98sorqlq1qjw8PBQXF6dr167p5MmT2rNnj6Kjo5NNw9yoUSO98MIL+uuvvyRJR48ela+vr5o3b65y5crJ0dFRISEh2rVrlw4cOCDpwU3Whg0bmkYnBAAggZ+fnxYvXqyTJ09K+l8gZsSIEclCF5GRkRozZky6AjGJOTk5qWXLlsaPgZs2bZKbm5vxep06dVSwYMGMviUAeGL17t1bM2bM0Ny5c7VhwwY1b95cJUqU0N27d3Xs2DFt3LjRFFqpUqWKaRTapLp06aJz585p6dKlkqSDBw+qVatWeumll/Tyyy/Ly8tLuXLlUlRUlG7evKm///5bx48f1+XLlx/p+7x3756WLl2qpUuXqkCBAqpcubLKlSsnDw8P5cqVS3fu3NGZM2e0detWU4ioQoUK8vPzs7rNZs2aqW3btlqyZImkB2Ghd999V7Vr11bt2rWVL18+hYWFadeuXdqzZ48pWN+hQwc1bdr0kb5nAHhUHB0dNXnyZLVv3163bt2SJC1btkzbt29Xs2bNVKpUKdnZ2SkoKEjr1683Ba3z5s2ryZMnG6PxWvPqq68qf/78RruFCxdq8eLF8vLyMkYolx58L0g6amvnzp21Z88eI9h5/vx5vfXWW6pfv76qV6+uPHny6Nq1a9q2bZuOHDliajtw4EBVqlQpYx9OCuzt7TVlyhS1b9/eCBoePHhQb7zxhqpXr67q1aurYMGCslgsunHjhv766y/t3r1bERER6RpRvl27dlq+fHmy9W3btk1Xf0uXLq3PPvtMgwYN0v3792WxWDR79mwtXrxYDRs2lI+Pj9zd3RUdHa3Lly/ryJEjOnTokOLj4zVs2LB07cua7Ho9AWSmzLwOz5Ejh3r06KEJEyZIevBwUPfu3VWnTh3jujQiIkLHjx/Xli1bFBkZKUkaMGCAzbOxZuVxoUGDBvrPf/6jXr16KSoqSnfv3tUHH3yg6dOnW50Ry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object>"]},"metadata":{"image/png":{"width":720,"height":540}}}]},{"cell_type":"markdown","source":["準備做最終的推論"],"metadata":{"id":"khmIFVEFhjf6"}},{"cell_type":"code","source":["import os\n","import sys\n","sys.path.append('/content/yolov7')\n","\n","import argparse\n","import time\n","from pathlib import Path\n","import cv2\n","import torch\n","import numpy as np\n","import torch.backends.cudnn as cudnn\n","from numpy import random\n","\n","from models.experimental import attempt_load\n","from utils.datasets import LoadStreams, LoadImages\n","from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \\\n","    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path\n","from utils.plots import plot_one_box\n","from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel\n","\n","\n","def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):\n","    # Resize and pad image while meeting stride-multiple constraints\n","    shape = img.shape[:2]  # current shape [height, width]\n","    if isinstance(new_shape, int):\n","        new_shape = (new_shape, new_shape)\n","\n","    # Scale ratio (new / old)\n","    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])\n","    if not scaleup:  # only scale down, do not scale up (for better test mAP)\n","        r = min(r, 1.0)\n","\n","    # Compute padding\n","    ratio = r, r  # width, height ratios\n","    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))\n","    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding\n","    if auto:  # minimum rectangle\n","        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding\n","    elif scaleFill:  # stretch\n","        dw, dh = 0.0, 0.0\n","        new_unpad = (new_shape[1], new_shape[0])\n","        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios\n","\n","    dw /= 2  # divide padding into 2 sides\n","    dh /= 2\n","\n","    if shape[::-1] != new_unpad:  # resize\n","        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)\n","    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))\n","    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))\n","    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border\n","    return img, ratio, (dw, dh)"],"metadata":{"id":"151oE9IrhQdp"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["注意weights和yaml檔位置，也可自行試試看不同的conf和IOU門檻值"],"metadata":{"id":"2C8yeicQhl5W"}},{"cell_type":"code","source":["classes_to_filter = None  #You can give list of classes to filter by name, Be happy you don't have to put class number. ['train','person' ]\n","\n","\n","opt  = {\n","    # 修改:權重位置很重要 很重要 很重要，尤其要注意exp\n","    \"weights\": \"runs/train/exp/weights/best.pt\", # Path to weights file default weights are for nano model\n","    \"yaml\"   : \"mydataset1000/data.yaml\",\n","    \"img-size\": 1280, # default image size\n","    \"conf-thres\": 0.47, # confidence threshold for inference.\n","    \"iou-thres\" : 0.66, # NMS IoU threshold for inference.\n","    \"device\" : '0',  # device to run our model i.e. 0 or 0,1,2,3 or cpu\n","    \"classes\" : classes_to_filter  # list of classes to filter or None\n","\n","}"],"metadata":{"id":"dVsesiQLhQiv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import cv2\n","import csv\n","\n","def myinfer(source_image_path, csv_dir='/content/output_csv', image_dir='/content/output_images'): \n","  \n","  with torch.no_grad():\n","    weights, imgsz = opt['weights'], opt['img-size']\n","    set_logging()\n","    device = select_device(opt['device'])\n","    half = device.type != 'cpu'\n","    model = attempt_load(weights, map_location=device)  # load FP32 model\n","    stride = int(model.stride.max())  # model stride\n","    imgsz = check_img_size(imgsz, s=stride)  # check img_size\n","    if half:\n","      model.half()\n","\n","    names = model.module.names if hasattr(model, 'module') else model.names\n","    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]\n","    if device.type != 'cpu':\n","      model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))\n","\n","    img0 = cv2.imread(source_image_path)\n","    img = letterbox(img0, imgsz, stride=stride)[0]\n","    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416\n","    img = np.ascontiguousarray(img)\n","    img = torch.from_numpy(img).to(device)\n","    img = img.half() if half else img.float()  # uint8 to fp16/32\n","    img /= 255.0  # 0 - 255 to 0.0 - 1.0\n","    if img.ndimension() == 3:\n","      img = img.unsqueeze(0)\n","\n","    # Inference\n","    t1 = time_synchronized()\n","    pred = model(img, augment= False)[0]\n","\n","    # Apply NMS\n","    classes = None\n","    if opt['classes']:\n","      classes = []\n","      for class_name in opt['classes']:\n","        classes.append(opt['classes'].index(class_name))\n","\n","\n","    pred = non_max_suppression(pred, opt['conf-thres'], opt['iou-thres'], classes= classes, agnostic= False)\n","    t2 = time_synchronized()\n","    for i, det in enumerate(pred):\n","      s = ''\n","      s += '%gx%g ' % img.shape[2:]  # print string\n","      gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]\n","      if len(det):\n","        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()\n","\n","        for c in det[:, -1].unique():\n","          n = (det[:, -1] == c).sum()  # detections per class\n","          s += f\"{n} {names[int(c)]}{'s' * (n > 1)}, \"  # add to string\n","      \n","        for *xyxy, conf, cls in reversed(det):\n","\n","          label = f'{names[int(cls)]} {conf:.2f}'\n","          plot_one_box(xyxy, img0, label=label, color=colors[int(cls)], line_thickness=3)\n","\n","  # 把推論圖片的結果用csv存下來\n","  name = source_image_path.split('/')[-1]\n","  img_name = name.split('.')[0]\n","  csv_name = csv_dir + '/' + name.split('.')[0] + \".csv\"\n","\n","  with open(csv_name, 'w', newline='') as csvfile:\n","\n","    # 以空白分隔欄位，建立 CSV 檔寫入器\n","    writer = csv.writer(csvfile, delimiter=',') # 逗號分隔\n","\n","    for i in pred[0].tolist():\n","      # i[0]左上x, i[1]左上y, i[2]右下x, i[3]右下y, i[4]信心, i[5]類別id      \n","      cls_id = int(i[5]) # 類別id\n","      bbox_LTx = int(np.round(i[0],0)) # 左上x\n","      bbox_LTy = int(np.round(i[1],0)) # 左上y\n","      bbox_W = int(np.round((i[2]-i[0]), 0)) # bbox寬\n","      bbox_H = int(np.round((i[3]-i[1]), 0)) # bbox高\n","      writer.writerow([img_name, cls_id, bbox_LTx, bbox_LTy, bbox_W, bbox_H ])\n","   \n","  output_path = image_dir  + '/' + name\n","  cv2.imwrite(output_path, img0)"],"metadata":{"id":"i6JGL9OyhrFt"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 上傳測試集"],"metadata":{"id":"eN-LqIl7huac"}},{"cell_type":"markdown","source":["(1)上傳Public Testing"],"metadata":{"id":"LJtpRZ6Khvj0"}},{"cell_type":"code","source":["'''\n","!gdown --id '1w8_OXLm3UvxNTYlliIvoz258iedpxH5-' --output data.zip\n","!unzip -q data.zip\n","os.remove(\"data.zip\")\n","'''\n","\n","!cp '/gdrive/MyDrive/Alex_Lin/無人機比賽/Public Testing Dataset_v2.zip' '/content/yolov7/Public Testing Dataset_v2.zip'\n","!unzip -q 'Public Testing Dataset_v2.zip'\n","os.remove(\"Public Testing Dataset_v2.zip\")"],"metadata":{"id":"luTb22IIhtWd"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["執行myinfer之前先檢查output路徑有沒有準備空的資料夾去接"],"metadata":{"id":"v1Kv7q6dhyTt"}},{"cell_type":"code","source":["import time\n","since = time.time() # 計時\n","\n","# test圖片所在的資料夾\n","thepath = \"/content/yolov7/public\"\n","\n","for image in os.listdir(thepath):\n","  #print(image)\n","  img_path = thepath + '/' + image\n","  myinfer(source_image_path=img_path, csv_dir='/content/public_csv', image_dir='/content/public_images')\n","\n","time_elapsed = time.time() - since\n","print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CJn2bf_0hw_B","executionInfo":{"status":"ok","timestamp":1670726048865,"user_tz":-480,"elapsed":692046,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"6ca01d38-6295-49c7-d88d-a9236239481f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fusing layers... \n","IAuxDetect.fuse\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n","  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"]},{"output_type":"stream","name":"stdout","text":["Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... 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layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... 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layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... 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layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Training complete in 11m 32s\n"]}]},{"cell_type":"markdown","source":["(2)上傳Private Testing"],"metadata":{"id":"S_VxspnMiA-W"}},{"cell_type":"code","source":["'''\n","!gdown --id '1DyZpY2AqTzHOYNg2L5SfbIuHF5l70e1F' --output data.zip\n","!unzip -q data.zip\n","os.remove(\"data.zip\")\n","'''\n","\n","!cp '/gdrive/MyDrive/Alex_Lin/無人機比賽/Private Testing Dataset_v2.zip' '/content/yolov7/Private Testing Dataset_v2.zip'\n","!unzip -q 'Private Testing Dataset_v2.zip'\n","os.remove(\"Private Testing Dataset_v2.zip\")"],"metadata":{"id":"Lzddz7daiCOg"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["執行myinfer之前先檢查output路徑有沒有準備空的資料夾去接"],"metadata":{"id":"0fJsLp3UiEV7"}},{"cell_type":"code","source":["import time\n","since = time.time() # 計時\n","\n","# test圖片所在的資料夾\n","thepath = \"/content/yolov7/Private Testing Dataset_v2\"\n","\n","for image in os.listdir(thepath):\n","  #print(image)\n","  img_path = thepath + '/' + image\n","  myinfer(source_image_path=img_path, csv_dir='/content/Private_csv', image_dir='/content/Private_images')\n","\n","time_elapsed = time.time() - since\n","print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VgnbYLhLiFts","executionInfo":{"status":"ok","timestamp":1670727006800,"user_tz":-480,"elapsed":689394,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"135dc54a-45e7-4a71-c53c-e4a62d23f005"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... 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layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... \n","IAuxDetect.fuse\n","Fusing layers... 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pd.concat([end,df],axis=0)\n","\n","    end.to_csv(output, index=False, header=False)\n","\n","\n","def concatcsv2(public_path=\"/content/public.csv\", Private_path=\"/content/Private.csv\", output=\"/content/yolo.csv\"):\n","  df1 = pd.read_csv(public_path, header=None)\n","  df2 = pd.read_csv(Private_path, header=None)\n","  end1 = pd.concat([df1,df2],axis=0)\n","  end1.to_csv(output, index=False, header=False)\n"],"metadata":{"id":"jQ1FdVYyiMs3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 把所有public的預測結果合併成一個csv\n","concatcsv(dir=\"/content/public_csv\", output=\"/content/public.csv\")"],"metadata":{"id":"saFMkvwfiN2m"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 把所有Private的預測結果合併成一個csv\n","concatcsv(dir=\"/content/Private_csv\", output=\"/content/Private.csv\")"],"metadata":{"id":"pUzCKEHaiOyd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 把public和Private合併，變成比賽要我們上傳的樣子\n","concatcsv2(public_path=\"/content/public.csv\", Private_path=\"/content/Private.csv\", output=\"/content/4A_my1000_4766.csv\")"],"metadata":{"id":"iCReVUmjiPlg"},"execution_count":null,"outputs":[]}]}