Merge branch 'main' into main

obss · Nov 5, 2023 · 487ef35 · 487ef35
2 parents 4fee50a + f04a7ee
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diff --git a/README.md b/README.md
@@ -30,7 +30,7 @@
 
 ## <div align="center">Overview</div>
 
-Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities.
+Object detection and instance segmentation are by far the most important applications in Computer Vision. However, the detection of small objects and inference on large images still need to be improved in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities.
 
 | Command  | Description  |
 |---|---|
@@ -39,12 +39,12 @@ Object detection and instance segmentation are by far the most important fields
 | [coco slice](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-slice-command-usage)  | automatically slice COCO annotation and image files |
 | [coco fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-fiftyone-command-usage)  | explore multiple prediction results on your COCO dataset with [fiftyone ui](https://github.com/voxel51/fiftyone) ordered by number of misdetections |
 | [coco evaluate](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-evaluate-command-usage)  | evaluate classwise COCO AP and AR for given predictions and ground truth |
-| [coco analyse](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-analyse-command-usage)  | calcualate and export many error analysis plots |
+| [coco analyse](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-analyse-command-usage)  | calculate and export many error analysis plots |
 | [coco yolov5](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-yolov5-command-usage)  | automatically convert any COCO dataset to [yolov5](https://github.com/ultralytics/yolov5) format |
 
 ## <div align="center">Quick Start Examples</div>
 
-[📜 List of publications that cite SAHI (currently 40+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)
+[📜 List of publications that cite SAHI (currently 100+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)
 
 [🏆 List of competition winners that used SAHI](https://github.com/obss/sahi/discussions/688)
 
@@ -126,17 +126,14 @@ conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.7 -c pytorch -c
 - Install your desired detection framework (yolov5):
 
 ```console
-pip install yolov5==7.0.4
+pip install yolov5==7.0.13
 ```
 
 - Install your desired detection framework (mmdet):
 
 ```console
-pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/index.html
-```
-
-```console
-pip install mmdet==2.26.0
+pip install mim
+mim install mmdet==3.0.0
 ```
 
 - Install your desired detection framework (detectron2):
@@ -270,4 +267,4 @@ python -m scripts.run_code_style format
 
 <a align="left" href="https://github.com/pranavdurai10" target="_blank">Pranav Durai</a>
 
-</div>
+</div>
diff --git a/sahi/__init__.py b/sahi/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "0.11.14"
+__version__ = "0.11.15"
 
 from sahi.annotation import BoundingBox, Category, Mask
 from sahi.auto_model import AutoDetectionModel

diff --git a/sahi/slicing.py b/sahi/slicing.py
@@ -469,7 +469,7 @@ def slice_coco(
     sliced_coco_images: List = []
 
     # iterate over images and slice
-    for coco_image in tqdm(coco.images):
+    for idx, coco_image in enumerate(tqdm(coco.images)):
         # get image path
         image_path: str = os.path.join(image_dir, coco_image.file_name)
         # get annotation json list corresponding to selected coco image
@@ -478,7 +478,7 @@ def slice_coco(
             slice_image_result = slice_image(
                 image=image_path,
                 coco_annotation_list=coco_image.annotations,
-                output_file_name=Path(coco_image.file_name).stem,
+                output_file_name=f"{Path(coco_image.file_name).stem}_{idx}",
                 output_dir=output_dir,
                 slice_height=slice_height,
                 slice_width=slice_width,

diff --git a/sahi/utils/yolov8.py b/sahi/utils/yolov8.py
@@ -14,11 +14,11 @@ class Yolov8TestConstants:
     YOLOV8M_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt"
     YOLOV8M_MODEL_PATH = "tests/data/models/yolov8/yolov8m.pt"
 
-    YOLOV8M_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt"
-    YOLOV8M_MODEL_PATH = "tests/data/models/yolov8/yolov8l.pt"
+    YOLOV8L_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt"
+    YOLOV8L_MODEL_PATH = "tests/data/models/yolov8/yolov8l.pt"
 
-    YOLOV8M_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt"
-    YOLOV8M_MODEL_PATH = "tests/data/models/yolov8/yolov8x.pt"
+    YOLOV8X_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt"
+    YOLOV8X_MODEL_PATH = "tests/data/models/yolov8/yolov8x.pt"
 
 
 def download_yolov8n_model(destination_path: Optional[str] = None):