Step3.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPhGhRn7aj/hWlVmqT7kCas"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"premium"},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/gdrive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2v_v_ZFZfsPU","executionInfo":{"status":"ok","timestamp":1670733703262,"user_tz":-480,"elapsed":15433,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"43dd16f7-5cdd-4406-fd8a-39fb2aa17f38"},"execution_count":1,"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":{"id":"JlCzB-6ZXis7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670733712761,"user_tz":-480,"elapsed":9502,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"9fb74bc3-f5f4-48c8-d19a-7ab4f2cb8f21"},"execution_count":2,"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 | 34.16 MiB/s, done.\n","Resolving deltas: 100% (523/523), 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 /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 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(7.9.0)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 35)) (5.4.8)\n","Collecting thop\n","  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (2.8.2)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (3.0.9)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (0.11.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (1.4.4)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from 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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":"SP76OPmqeq4O","executionInfo":{"status":"ok","timestamp":1670733713020,"user_tz":-480,"elapsed":263,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"4189f018-486c-478e-adaa-06a8441f7d3e"},"execution_count":3,"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":"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":"zW7n6tvUeq60","executionInfo":{"status":"ok","timestamp":1670733713303,"user_tz":-480,"elapsed":290,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"9c9c9ca6-4822-411b-a70e-8bd04091c096"},"execution_count":4,"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","# 無人機dataset\n","'''\n","!pip install -q --upgrade gdown\n","!gdown --id '1HIC6u_mBvkxdR8F9-sTlKkLXeAhyzC1G'\n","!unzip -q VisDrone4.zip\n","os.remove(\"VisDrone4.zip\")\n","'''\n","\n","!cp '/gdrive/MyDrive/Alex_Lin/無人機比賽/VisDrone4.zip' '/content/yolov7/VisDrone4.zip'\n","!unzip -q VisDrone4.zip\n","os.remove(\"VisDrone4.zip\")"],"metadata":{"id":"ewYQ44n-Xr3t","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670733743875,"user_tz":-480,"elapsed":30575,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"7b257539-da16-43e4-cea5-d5f910719871"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/yolov7\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"to3Y_G7ngf6Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["yaml_file = \"/content/yolov7/VisDrone4/data.yaml\"\n","if os.path.exists(yaml_file) == False:\n","  f= open(yaml_file,\"w+\")\n","  f.write(\"train: /content/yolov7/VisDrone4/train/images\\n\") # train資料位置\n","  f.write(\"val: /content/yolov7/VisDrone4/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":"KGDb3jIrXyGH","executionInfo":{"status":"ok","timestamp":1670733743876,"user_tz":-480,"elapsed":4,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["%cd /content/yolov7"],"metadata":{"id":"41tXR12-X4HU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670733743876,"user_tz":-480,"elapsed":4,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"8a65c5a4-45a2-40e7-a7ea-507dcf454f8a"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/yolov7\n"]}]},{"cell_type":"code","source":["!pwd"],"metadata":{"id":"SQMN-QipX5Tc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670733744273,"user_tz":-480,"elapsed":7,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"94e3c010-f0b8-41e0-ce71-de5b487dfe47"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/yolov7\n"]}]},{"cell_type":"markdown","source":["下載前一階段在無人機訓練4類別epoch的權重檔準備接類訓練"],"metadata":{"id":"n0bkGbp7X-IY"}},{"cell_type":"code","source":["''' gdown 常常很奇怪\n","!gdown --id '1FWlpxiI-ai1GmSMBk_zqKfRwQD3un-0H' --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/2_best.zip' '/content/yolov7/2_best.zip'\n","!unzip -q 2_best.zip\n","os.remove(\"2_best.zip\")"],"metadata":{"id":"kBK2n-RJYGHF","executionInfo":{"status":"ok","timestamp":1670733748609,"user_tz":-480,"elapsed":4338,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"rocNQ7c5eut-"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["換成VisDrone dataset的Anchor box尺寸"],"metadata":{"id":"isKG_1tLev3B"}},{"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":"QribeXtleuwz","executionInfo":{"status":"ok","timestamp":1670733749078,"user_tz":-480,"elapsed":477,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"fbaa4c56-9101-469a-aab5-ea9553c91abd"},"execution_count":10,"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":["# 修改這幾行內容 注意跳脫字元\n","!sed -i '8s/19,27,  44,40,  38,94/4,9, 5,16, 6,25/' cfg/training/yolov7-w6.yaml\n","!sed -i '9s/96,68,  86,152,  180,137/11,14, 10,35, 16,27/' cfg/training/yolov7-w6.yaml\n","!sed -i '10s/140,301,  303,264,  238,542/25,19, 19,51, 37,35/' cfg/training/yolov7-w6.yaml\n","!sed -i '11s/436,615,  739,380,  925,792/34,82, 64,58, 97,130/' 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":"FEL9a5uaeu1c","executionInfo":{"status":"ok","timestamp":1670733749334,"user_tz":-480,"elapsed":258,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"434a100b-9dc6-4bc8-f830-cf46b755c276"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["  - [ 4,9, 5,16, 6,25 ]  # P3/8\n","  - [ 11,14, 10,35, 16,27 ]  # P4/16\n","  - [ 25,19, 19,51, 37,35 ]  # P5/32\n","  - [ 34,82, 64,58, 97,130 ]  # P6/64\n"]}]},{"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":"ny9s9ufae2mi"}},{"cell_type":"code","source":["!pip install wandb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MNGJQ1mte3sU","executionInfo":{"status":"ok","timestamp":1670733758637,"user_tz":-480,"elapsed":9307,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"683bb892-79bf-4fe6-e7fd-46ab47774bf1"},"execution_count":12,"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 4.8 MB/s 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sha256=9040f2ad285210f5241efb14dd913e055c8d610a3670ab467cf6186c2027e984\n","  Stored in directory: /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":"JJ96Ozzge6pn","executionInfo":{"status":"ok","timestamp":1670733767510,"user_tz":-480,"elapsed":8888,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"e9b46591-3ebb-44f9-e140-515a3438ebf1"},"execution_count":13,"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-20221211_044246-2h3ksazk</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/2h3ksazk\" target=\"_blank\">magic-bird-12</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":["id "],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":38},"id":"7K2no2cde7uc","executionInfo":{"status":"ok","timestamp":1670733767511,"user_tz":-480,"elapsed":23,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"fe65dea7-7e00-4e22-d9ba-b0959b0839f1"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2g0g4su6'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["# 注意--data和--weights的路徑\n","\n","!python train_aux.py --epochs 60 \\\n","--batch-size 22 \\\n","--data VisDrone4/data.yaml \\\n","--img 1280 1280 \\\n","--device 0 \\\n","--cfg cfg/training/yolov7-w6.yaml \\\n","--weights '2_best.pt' \\\n","--hyp data/hyp.scratch.custom.yaml\n","\n","# A100、1280、BS22 大約一個epoch 6分12秒"],"metadata":{"id":"gr6p6Hd5exJ_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670119504916,"user_tz":-480,"elapsed":22767728,"user":{"displayName":"yu kuo","userId":"15852834483727446797"}},"outputId":"86c715f5-53a2-493e-f4bd-1470ffad3d63"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["YOLOR 🚀 v0.1-104-g941b94c torch 1.12.1+cu113 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='VisDrone4/data.yaml', device='0', entity=None, epochs=60, 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, weights='2_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","2022-12-03 19:45:44.100146: 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[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\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      7040  models.common.Conv                      [12, 64, 3, 1]                \n","  2                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n","  3                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n","  4                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n","  5                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  9  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 10                -1  1     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    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  [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, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [256, 512, 768, 1024, 320, 640, 960, 1280]]\n","/usr/local/lib/python3.8/dist-packages/torch/functional.py:478: 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:2894.)\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 2_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/VisDrone4/train/labels' images and labels... 194 found, 0 missing, 0 empty, 0 corrupted:   3% 194/6471 [00:00<00:03, 1937.07it/s]\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/VisDrone4/train/images/0000137_02220_d_0000163.jpg: duplicate labels\n","\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/VisDrone4/train/images/0000140_00118_d_0000002.jpg: duplicate labels\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov7/VisDrone4/train/labels' images and labels... 1993 found, 0 missing, 0 empty, 2 corrupted:  31% 1993/6471 [00:00<00:01, 2633.16it/s]\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/VisDrone4/train/images/9999945_00000_d_0000114.jpg: duplicate labels\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov7/VisDrone4/train/labels' images and labels... 4348 found, 0 missing, 0 empty, 3 corrupted:  67% 4348/6471 [00:01<00:00, 2808.02it/s]\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/VisDrone4/train/images/9999972_00000_d_0000158.jpg: duplicate labels\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov7/VisDrone4/train/labels' images and labels... 4646 found, 0 missing, 0 empty, 4 corrupted:  72% 4646/6471 [00:01<00:00, 2856.16it/s]\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/VisDrone4/train/images/9999981_00000_d_0000047.jpg: duplicate labels\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov7/VisDrone4/train/labels' images and labels... 5231 found, 0 missing, 0 empty, 5 corrupted:  81% 5231/6471 [00:01<00:00, 2887.32it/s]\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label /content/yolov7/VisDrone4/train/images/9999987_00000_d_0000049.jpg: duplicate labels\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov7/VisDrone4/train/labels' images and labels... 6471 found, 0 missing, 0 empty, 6 corrupted: 100% 6471/6471 [00:02<00:00, 2705.01it/s]\n","\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov7/VisDrone4/train/labels.cache\n","\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/yolov7/VisDrone4/val/labels' images and labels... 548 found, 0 missing, 0 empty, 0 corrupted: 100% 548/548 [00:00<00:00, 1549.76it/s]\n","\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/yolov7/VisDrone4/val/labels.cache\n","\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 2.95, Best Possible Recall (BPR) = 0.9329. Attempting to improve anchors, please wait...\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mWARNING: Extremely small objects found. 3092 of 323872 labels are < 3 pixels in size.\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mRunning kmeans for 12 anchors on 323864 points...\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 0.9990 best possible recall, 6.72 anchors past thr\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mn=12, img_size=1280, metric_all=0.328/0.756-mean/best, past_thr=0.479-mean: 8,9,  10,19,  21,14,  18,30,  37,22,  29,45,  60,34,  49,71,  96,54,  83,119,  164,89,  189,197\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7707: 100% 1000/1000 [00:23<00:00, 42.92it/s]\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mthr=0.25: 0.9996 best possible recall, 7.20 anchors past thr\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mn=12, img_size=1280, metric_all=0.347/0.771-mean/best, past_thr=0.480-mean: 6,7,  7,16,  15,12,  12,24,  28,16,  22,34,  48,27,  36,51,  77,41,  63,77,  116,72,  136,120\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mNew anchors saved to model. Update model *.yaml to use these anchors in the future.\n","\n","Image sizes 1280 train, 1280 test\n","Using 8 dataloader workers\n","Logging results to runs/train/exp\n","Starting training for 60 epochs...\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      0/59     38.7G    0.0539   0.09159  0.003779    0.1493      1197      1280: 100% 294/294 [06:40<00:00,  1.36s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:15<00:00,  1.18s/it]\n","                 all         548       35895       0.746       0.648       0.681       0.382\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      1/59     38.9G   0.04207   0.09088   0.00284    0.1358      1642      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.39it/s]\n","                 all         548       35895       0.749       0.666       0.688       0.393\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      2/59     38.9G   0.04255   0.09169  0.002588    0.1368      1241      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.36it/s]\n","                 all         548       35895       0.744       0.673       0.699       0.398\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      3/59     39.2G   0.04305   0.09326  0.002509    0.1388      1298      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.37it/s]\n","                 all         548       35895       0.722       0.672       0.693        0.39\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      4/59     39.2G   0.04215   0.09196  0.002306    0.1364      1046      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.35it/s]\n","                 all         548       35895       0.779       0.653       0.705       0.403\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      5/59     39.2G   0.04155   0.09219  0.002203    0.1359      1739      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.32it/s]\n","                 all         548       35895       0.768       0.649       0.699         0.4\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      6/59     39.2G   0.04134   0.09106  0.002208    0.1346      1650      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.36it/s]\n","                 all         548       35895       0.758       0.661       0.708       0.406\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      7/59     39.2G   0.04097   0.09149  0.002108    0.1346      1282      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.40it/s]\n","                 all         548       35895       0.777        0.65       0.708       0.409\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      8/59     39.2G   0.04077   0.08954   0.00208    0.1324      1345      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.41it/s]\n","                 all         548       35895       0.735       0.671         0.7       0.405\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      9/59     39.2G   0.04065   0.09111  0.001997    0.1338      1274      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.41it/s]\n","                 all         548       35895       0.748       0.656       0.699       0.403\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     10/59     39.2G   0.04108   0.09179  0.002113     0.135      1781      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.35it/s]\n","                 all         548       35895       0.772       0.652       0.707        0.41\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     11/59     39.2G   0.04085   0.09308  0.002006    0.1359      1596      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.35it/s]\n","                 all         548       35895       0.738       0.668       0.704       0.406\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     12/59     39.2G   0.04052   0.09261  0.001911     0.135       935      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.37it/s]\n","                 all         548       35895       0.748       0.675       0.708       0.409\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     13/59     39.2G   0.04039   0.09425  0.001888    0.1365      1306      1280: 100% 294/294 [06:07<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.38it/s]\n","                 all         548       35895       0.764       0.662        0.71       0.412\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     14/59     39.2G   0.03991    0.0927  0.001772    0.1344      1678      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.41it/s]\n","                 all         548       35895       0.794       0.659       0.717       0.416\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     15/59     39.2G   0.03981   0.09243  0.001719     0.134      2368      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.39it/s]\n","                 all         548       35895       0.777       0.657       0.714       0.416\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     16/59     39.2G   0.03974   0.09208  0.001775    0.1336      1321      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.40it/s]\n","                 all         548       35895       0.792       0.658       0.712       0.413\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     17/59     39.2G   0.03961   0.09162  0.001733     0.133      1367      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.37it/s]\n","                 all         548       35895       0.772       0.666       0.717       0.417\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     18/59     39.2G   0.03924   0.09084  0.001618    0.1317      1194      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.36it/s]\n","                 all         548       35895       0.784       0.667       0.722       0.421\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     19/59     39.2G   0.03931   0.09166  0.001661    0.1326      1258      1280: 100% 294/294 [06:06<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.41it/s]\n","                 all         548       35895       0.772       0.674       0.717       0.417\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     20/59     39.2G   0.03927   0.09247  0.001601    0.1333      1476      1280: 100% 294/294 [06:09<00:00,  1.26s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.40it/s]\n","                 all         548       35895       0.764       0.684       0.723       0.422\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     21/59     39.2G    0.0389   0.08894  0.001585    0.1294      1809      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.43it/s]\n","                 all         548       35895       0.769       0.684       0.721       0.422\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     22/59     39.2G   0.03872   0.09025  0.001579    0.1306      1436      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.40it/s]\n","                 all         548       35895       0.763       0.687       0.721       0.424\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     23/59     39.2G   0.03889   0.08911  0.001517    0.1295      1782      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.40it/s]\n","                 all         548       35895       0.792       0.661       0.719       0.421\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     24/59     39.2G   0.03891   0.08982  0.001496    0.1302      1613      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.44it/s]\n","                 all         548       35895       0.779       0.672       0.721       0.421\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     25/59     39.2G   0.03889    0.0897  0.001529    0.1301      1536      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.43it/s]\n","                 all         548       35895       0.786       0.672       0.721       0.424\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     26/59     39.2G   0.03858   0.08883  0.001515    0.1289      1038      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.43it/s]\n","                 all         548       35895       0.778       0.666       0.714       0.419\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     27/59     39.2G   0.03866   0.08756  0.001465    0.1277      2102      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.43it/s]\n","                 all         548       35895       0.772       0.683       0.719       0.423\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     28/59     39.2G   0.03839   0.08802  0.001412    0.1278      1406      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.43it/s]\n","                 all         548       35895       0.748       0.697       0.719       0.422\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     29/59     39.2G   0.03819   0.08762  0.001376    0.1272      1534      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.44it/s]\n","                 all         548       35895       0.782       0.678       0.719       0.423\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     30/59     39.2G   0.03806   0.08768  0.001352    0.1271      1234      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.44it/s]\n","                 all         548       35895       0.765       0.685       0.718       0.422\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     31/59     39.2G   0.03819   0.08789   0.00137    0.1275      1067      1280: 100% 294/294 [06:06<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.44it/s]\n","                 all         548       35895       0.775       0.682       0.719       0.423\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     32/59     39.2G   0.03794    0.0861  0.001339    0.1254      1666      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:09<00:00,  1.44it/s]\n","                 all         548       35895       0.774       0.692       0.726       0.427\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     33/59     39.2G   0.03783   0.08511  0.001279    0.1242      1159      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.45it/s]\n","                 all         548       35895       0.782       0.678        0.72       0.425\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     34/59     39.2G    0.0377     0.087  0.001252    0.1259      1153      1280: 100% 294/294 [06:07<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.778       0.677       0.718       0.424\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     35/59     39.2G   0.03794   0.08702   0.00131    0.1263      1672      1280: 100% 294/294 [06:06<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.47it/s]\n","                 all         548       35895       0.799       0.664       0.718       0.425\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     36/59     39.2G   0.03769   0.08512  0.001262    0.1241      1413      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.776       0.673       0.714       0.423\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     37/59     39.2G   0.03768   0.08578  0.001261    0.1247      1386      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.49it/s]\n","                 all         548       35895       0.779       0.684       0.718       0.425\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     38/59     39.2G   0.03737   0.08532  0.001217    0.1239      1366      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.49it/s]\n","                 all         548       35895       0.791       0.678       0.721       0.427\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     39/59     39.2G   0.03724   0.08603  0.001181    0.1244      1735      1280: 100% 294/294 [06:07<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.773       0.684       0.718       0.425\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     40/59     39.2G   0.03741   0.08359  0.001211    0.1222      1209      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.47it/s]\n","                 all         548       35895       0.775       0.681        0.72       0.427\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     41/59     39.2G   0.03709   0.08371  0.001204     0.122      1821      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.47it/s]\n","                 all         548       35895       0.784       0.676       0.719       0.425\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     42/59     39.2G    0.0372   0.08464  0.001164     0.123      1201      1280: 100% 294/294 [06:06<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.46it/s]\n","                 all         548       35895       0.792       0.668       0.717       0.426\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     43/59     39.2G   0.03682   0.08387   0.00111    0.1218      1053      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.47it/s]\n","                 all         548       35895       0.776       0.681       0.717       0.427\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     44/59     39.2G   0.03664   0.08268  0.001144    0.1205      1222      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.47it/s]\n","                 all         548       35895       0.789       0.673       0.719       0.428\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     45/59     39.2G   0.03682    0.0832  0.001109    0.1211      1535      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.784        0.68       0.718       0.429\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     46/59     39.2G   0.03688    0.0838  0.001079    0.1218      1514      1280: 100% 294/294 [06:08<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.769       0.687       0.719       0.429\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     47/59     39.2G   0.03684   0.08229  0.001103    0.1202      1395      1280: 100% 294/294 [06:05<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.49it/s]\n","                 all         548       35895       0.776       0.685       0.715       0.425\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     48/59     39.2G    0.0366   0.08299  0.001079    0.1207      1486      1280: 100% 294/294 [06:07<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.789       0.673       0.715       0.426\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     49/59     39.2G    0.0367   0.08392  0.001065    0.1217      1660      1280: 100% 294/294 [06:09<00:00,  1.26s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.47it/s]\n","                 all         548       35895       0.773       0.686       0.716       0.426\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     50/59     39.2G    0.0366    0.0803  0.001045    0.1179      1906      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.778       0.682       0.716       0.428\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     51/59     39.2G   0.03658   0.08187  0.001058    0.1195      1136      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.49it/s]\n","                 all         548       35895       0.783       0.682       0.717        0.43\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     52/59     39.2G   0.03649   0.08243  0.001038      0.12      1779      1280: 100% 294/294 [06:06<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.50it/s]\n","                 all         548       35895       0.785       0.677       0.715       0.427\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     53/59     39.2G    0.0365   0.08203  0.001035    0.1196      1107      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.50it/s]\n","                 all         548       35895       0.797       0.668       0.711       0.425\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     54/59     39.2G   0.03624   0.08151  0.001039    0.1188      1601      1280: 100% 294/294 [06:04<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.49it/s]\n","                 all         548       35895       0.768       0.692       0.716       0.428\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     55/59     39.2G    0.0364   0.08217  0.001024    0.1196      1802      1280: 100% 294/294 [06:06<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.48it/s]\n","                 all         548       35895       0.768       0.688       0.712       0.426\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     56/59     39.2G   0.03616    0.0809  0.001022    0.1181      1350      1280: 100% 294/294 [06:06<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.47it/s]\n","                 all         548       35895       0.778       0.683       0.715       0.428\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     57/59     39.2G   0.03621    0.0823  0.001021    0.1195      1705      1280: 100% 294/294 [06:07<00:00,  1.25s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.50it/s]\n","                 all         548       35895       0.792       0.676       0.715       0.427\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     58/59     39.2G    0.0361   0.08048 0.0009857    0.1176      1210      1280: 100% 294/294 [06:02<00:00,  1.23s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:08<00:00,  1.53it/s]\n","                 all         548       35895       0.783        0.68       0.713       0.426\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     59/59     39.2G   0.03619    0.0803 0.0009973    0.1175      1281      1280: 100% 294/294 [06:03<00:00,  1.24s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 13/13 [00:20<00:00,  1.55s/it]\n","                 all         548       35895       0.791       0.669       0.712       0.424\n","                 car         548       16039       0.895       0.867       0.913       0.651\n","                 hov         548        1001       0.736        0.53        0.57       0.395\n","              person         548       13969       0.799        0.67        0.72       0.338\n","          motorcycle         548        4886       0.733       0.608       0.645       0.312\n","60 epochs completed in 6.305 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"]}]},{"cell_type":"markdown","source":["都跑完時到側邊欄下載權重檔"],"metadata":{"id":"ROtoLb-P6-q-"}},{"cell_type":"code","source":[],"metadata":{"id":"dvTEc4tNZtoA"},"execution_count":null,"outputs":[]}]}