Ultralytics Code Refactor https://ultralytics.com/actions (#13191)
* Refactor code for speed and clarity * Auto-format by https://ultralytics.com/actions --------- Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn JocherandUltralyticsAssistant authored and GitHub committed
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@@ -66,8 +66,8 @@ def save_one_txt(predn, save_conf, shape, file):
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Saves one detection result to a txt file in normalized xywh format, optionally including confidence.
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Args:
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predn (torch.Tensor): Predicted bounding boxes and associated confidence scores and classes in xyxy format,
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tensor of shape (N, 6) where N is the number of detections.
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predn (torch.Tensor): Predicted bounding boxes and associated confidence scores and classes in xyxy format, tensor
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of shape (N, 6) where N is the number of detections.
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save_conf (bool): If True, saves the confidence scores along with the bounding box coordinates.
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shape (tuple): Shape of the original image as (height, width).
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file (str | Path): File path where the result will be saved.
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@@ -219,7 +219,7 @@ def run(
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Evaluates a YOLOv5 model on a dataset and logs performance metrics.
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Args:
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data (str | dict): Path to a dataset yaml file or a dataset dictionary.
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data (str | dict): Path to a dataset YAML file or a dataset dictionary.
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weights (str | list[str], optional): Path to the model weights file(s). Supports various formats including PyTorch,
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TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow SavedModel, TensorFlow GraphDef, TensorFlow Lite,
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TensorFlow Edge TPU, and PaddlePaddle.
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@@ -469,34 +469,34 @@ def run(
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def parse_opt():
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"""
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Parses command-line options for YOLOv5 model inference configuration.
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Parse command-line options for configuring YOLOv5 model inference.
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Args:
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data (str): Path to the dataset YAML file, default is 'data/coco128.yaml'.
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weights (list[str]): List of paths to the model weight files, default is 'yolov5s.pt'.
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batch_size (int): Batch size for inference, default is 32.
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imgsz (int): Inference image size in pixels, default is 640.
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conf_thres (float): Confidence threshold for predictions, default is 0.001.
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iou_thres (float): IoU threshold for Non-Max Suppression (NMS), default is 0.6.
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max_det (int): Maximum number of detections per image, default is 300.
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task (str): Task type - options are 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.
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device (str): Device to run the model on, e.g., '0' or '0,1,2,3' or 'cpu'. Default is empty to let the system choose automatically.
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workers (int): Maximum number of dataloader workers per rank in DDP mode, default is 8.
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single_cls (bool): If set, treats the dataset as a single-class dataset. Default is False.
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augment (bool): If set, performs augmented inference. Default is False.
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verbose (bool): If set, reports mAP by class. Default is False.
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save_txt (bool): If set, saves results to *.txt files. Default is False.
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save_hybrid (bool): If set, saves label+prediction hybrid results to *.txt files. Default is False.
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save_conf (bool): If set, saves confidences in --save-txt labels. Default is False.
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save_json (bool): If set, saves results to a COCO-JSON file. Default is False.
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project (str): Project directory to save results to. Default is 'runs/val'.
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name (str): Name of the directory to save results to. Default is 'exp'.
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exist_ok (bool): If set, existing directory will not be incremented. Default is False.
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half (bool): If set, uses FP16 half-precision inference. Default is False.
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dnn (bool): If set, uses OpenCV DNN for ONNX inference. Default is False.
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data (str, optional): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
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weights (list[str], optional): List of paths to model weight files. Default is 'yolov5s.pt'.
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batch_size (int, optional): Batch size for inference. Default is 32.
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imgsz (int, optional): Inference image size in pixels. Default is 640.
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conf_thres (float, optional): Confidence threshold for predictions. Default is 0.001.
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iou_thres (float, optional): IoU threshold for Non-Max Suppression (NMS). Default is 0.6.
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max_det (int, optional): Maximum number of detections per image. Default is 300.
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task (str, optional): Task type - options are 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.
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device (str, optional): Device to run the model on. e.g., '0' or '0,1,2,3' or 'cpu'. Default is empty to let the system choose automatically.
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workers (int, optional): Maximum number of dataloader workers per rank in DDP mode. Default is 8.
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single_cls (bool, optional): If set, treats the dataset as a single-class dataset. Default is False.
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augment (bool, optional): If set, performs augmented inference. Default is False.
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verbose (bool, optional): If set, reports mAP by class. Default is False.
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save_txt (bool, optional): If set, saves results to *.txt files. Default is False.
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save_hybrid (bool, optional): If set, saves label+prediction hybrid results to *.txt files. Default is False.
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save_conf (bool, optional): If set, saves confidences in --save-txt labels. Default is False.
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save_json (bool, optional): If set, saves results to a COCO-JSON file. Default is False.
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project (str, optional): Project directory to save results to. Default is 'runs/val'.
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name (str, optional): Name of the directory to save results to. Default is 'exp'.
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exist_ok (bool, optional): If set, existing directory will not be incremented. Default is False.
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half (bool, optional): If set, uses FP16 half-precision inference. Default is False.
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dnn (bool, optional): If set, uses OpenCV DNN for ONNX inference. Default is False.
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Returns:
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argparse.Namespace: Parsed command-line options
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argparse.Namespace: Parsed command-line options.
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Notes:
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- The '--data' parameter is checked to ensure it ends with 'coco.yaml' if '--save-json' is set.
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@@ -508,7 +508,7 @@ def parse_opt():
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```python
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$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
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```
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Different model formats could be used instead of yolov5s.pt:
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Different model formats could be used instead of `yolov5s.pt`:
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```python
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$ python val.py --weights yolov5s.pt yolov5s.torchscript yolov5s.onnx yolov5s_openvino_model yolov5s.engine
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```
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@@ -551,17 +551,16 @@ def main(opt):
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Args:
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opt (argparse.Namespace): Parsed command-line options.
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This includes values for parameters like 'data', 'weights', 'batch_size', 'imgsz', 'conf_thres', 'iou_thres',
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'max_det', 'task', 'device', 'workers', 'single_cls', 'augment', 'verbose', 'save_txt', 'save_hybrid',
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'save_conf', 'save_json', 'project', 'name', 'exist_ok', 'half', and 'dnn', essential for configuring
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the YOLOv5 tasks.
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This includes values for parameters like 'data', 'weights', 'batch_size', 'imgsz', 'conf_thres',
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'iou_thres', 'max_det', 'task', 'device', 'workers', 'single_cls', 'augment', 'verbose', 'save_txt',
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'save_hybrid', 'save_conf', 'save_json', 'project', 'name', 'exist_ok', 'half', and 'dnn', essential
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for configuring the YOLOv5 tasks.
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Returns:
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None
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Examples:
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To validate a trained YOLOv5 model on the COCO dataset with a specific weights file, use:
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```python
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$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
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```
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