Ultralytics Code Refactor https://ultralytics.com/actions (#13175)
* Refactor code for speed and clarity * Update export.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update export.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add missing Python function docstrings * Auto-format by https://ultralytics.com/actions * Update export.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add missing Python function docstrings * Auto-format by https://ultralytics.com/actions --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@@ -101,40 +101,40 @@ def run(
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Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc.
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Args:
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weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'.
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source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam index.
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Default is 'data/images'.
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data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
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imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640).
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conf_thres (float): Confidence threshold for detections. Default is 0.25.
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iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45.
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max_det (int): Maximum number of detections per image. Default is 1000.
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device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which
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uses the best available device.
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view_img (bool): If True, display inference results using OpenCV. Default is False.
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save_txt (bool): If True, save results in a text file. Default is False.
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save_csv (bool): If True, save results in a CSV file. Default is False.
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save_conf (bool): If True, include confidence scores in the saved results. Default is False.
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save_crop (bool): If True, save cropped prediction boxes. Default is False.
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nosave (bool): If True, do not save inference images or videos. Default is False.
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classes (list[int]): List of class indices to filter detections by. Default is None.
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agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False.
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augment (bool): If True, use augmented inference. Default is False.
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visualize (bool): If True, visualize feature maps. Default is False.
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update (bool): If True, update all models' weights. Default is False.
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project (str | Path): Directory to save results. Default is 'runs/detect'.
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name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'.
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exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is
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False.
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line_thickness (int): Thickness of bounding box lines in pixels. Default is 3.
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hide_labels (bool): If True, do not display labels on bounding boxes. Default is False.
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hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False.
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half (bool): If True, use FP16 half-precision inference. Default is False.
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dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False.
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vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1.
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weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'.
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source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam index.
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Default is 'data/images'.
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data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
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imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640).
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conf_thres (float): Confidence threshold for detections. Default is 0.25.
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iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45.
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max_det (int): Maximum number of detections per image. Default is 1000.
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device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which
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uses the best available device.
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view_img (bool): If True, display inference results using OpenCV. Default is False.
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save_txt (bool): If True, save results in a text file. Default is False.
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save_csv (bool): If True, save results in a CSV file. Default is False.
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save_conf (bool): If True, include confidence scores in the saved results. Default is False.
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save_crop (bool): If True, save cropped prediction boxes. Default is False.
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nosave (bool): If True, do not save inference images or videos. Default is False.
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classes (list[int]): List of class indices to filter detections by. Default is None.
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agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False.
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augment (bool): If True, use augmented inference. Default is False.
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visualize (bool): If True, visualize feature maps. Default is False.
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update (bool): If True, update all models' weights. Default is False.
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project (str | Path): Directory to save results. Default is 'runs/detect'.
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name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'.
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exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is
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False.
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line_thickness (int): Thickness of bounding box lines in pixels. Default is 3.
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hide_labels (bool): If True, do not display labels on bounding boxes. Default is False.
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hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False.
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half (bool): If True, use FP16 half-precision inference. Default is False.
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dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False.
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vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1.
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Returns:
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None
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None
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Examples:
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```python
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@@ -397,15 +397,15 @@ def main(opt):
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Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running.
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Args:
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opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. See function `parse_opt` for details.
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opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. See function `parse_opt` for details.
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Returns:
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None
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None
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Note:
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This function performs essential pre-execution checks and initiates the YOLOv5 detection process based on user-specified options.
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Refer to the usage guide and examples for more information about different sources and formats at:
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https://github.com/ultralytics/ultralytics
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This function performs essential pre-execution checks and initiates the YOLOv5 detection process based on user-specified options.
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Refer to the usage guide and examples for more information about different sources and formats at:
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https://github.com/ultralytics/ultralytics
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Example usage:
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