pocketpose.benchmarks.eval_coco
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Module Contents#
Functions#
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Get the list of image paths from the dataset folder. |
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Evaluate model results using COCO API. |
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Save the results as Markdown and LaTeX tables. |
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Plot the benchmarking results. |
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- pocketpose.benchmarks.eval_coco.load_model(model_name, return_vis, kpt_thr)#
- pocketpose.benchmarks.eval_coco.get_image_list(dataset_path)#
Get the list of image paths from the dataset folder.
- Args:
dataset_path (str): The path to the dataset folder.
- Returns:
list: The list of image paths.
- pocketpose.benchmarks.eval_coco.infer_coco(model_name, images_path, save_dir, dataset_type='coco_sp', det_annos_path=None, kpt_thr=0.3, save_vis=False)#
- pocketpose.benchmarks.eval_coco.eval_coco_keypoints(annos_path, pred_path)#
Evaluate model results using COCO API.
- Args:
annos_path (str): The path to the ground-truth annotations file. pred_path (str): The path to the predictions file.
- Returns:
- dict: The evaluation results as a dictionary with the following keys:
AP: Average precision with IoU threshold of 0.5:0.95.
AP^{50}: Average precision with IoU threshold of 0.5.
AP^{75}: Average precision with IoU threshold of 0.75.
AP^{M}: Average precision with IoU threshold of 0.5:0.95 (medium objects).
AP^{L}: Average precision with IoU threshold of 0.5:0.95 (large objects).
AR: Average recall with IoU threshold of 0.5:0.95.
AR^{50}: Average recall with IoU threshold of 0.5.
AR^{75}: Average recall with IoU threshold of 0.75.
AR^{M}: Average recall with IoU threshold of 0.5:0.95 (medium objects).
AR^{L}: Average recall with IoU threshold of 0.5:0.95 (large objects).
- pocketpose.benchmarks.eval_coco.update_results(model_name, results, save_dir)#
- pocketpose.benchmarks.eval_coco.create_tables(results_dict, save_dir)#
Save the results as Markdown and LaTeX tables.
- pocketpose.benchmarks.eval_coco.plot_results(results_dict, save_dir)#
Plot the benchmarking results.
- pocketpose.benchmarks.eval_coco.benchmark(model_name, images_path, annos_path, save_dir, dataset_type='coco_sp', det_annos_path=None, kpt_thr=0.3, save_vis=False)#