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74 lines
1.4 KiB
74 lines
1.4 KiB
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description: Learn how to work with Ultralytics YOLO Detection, Segmentation & Classification Models, load weights and parse models in PyTorch. |
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keywords: neural network, deep learning, computer vision, object detection, image segmentation, image classification, model ensemble, PyTorch |
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## BaseModel |
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### ::: ultralytics.nn.tasks.BaseModel |
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<br><br> |
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## DetectionModel |
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### ::: ultralytics.nn.tasks.DetectionModel |
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<br><br> |
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## SegmentationModel |
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### ::: ultralytics.nn.tasks.SegmentationModel |
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<br><br> |
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## PoseModel |
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### ::: ultralytics.nn.tasks.PoseModel |
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<br><br> |
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## ClassificationModel |
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### ::: ultralytics.nn.tasks.ClassificationModel |
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<br><br> |
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## RTDETRDetectionModel |
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### ::: ultralytics.nn.tasks.RTDETRDetectionModel |
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## Ensemble |
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### ::: ultralytics.nn.tasks.Ensemble |
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<br><br> |
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## torch_safe_load |
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### ::: ultralytics.nn.tasks.torch_safe_load |
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## attempt_load_weights |
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### ::: ultralytics.nn.tasks.attempt_load_weights |
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## attempt_load_one_weight |
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### ::: ultralytics.nn.tasks.attempt_load_one_weight |
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## parse_model |
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### ::: ultralytics.nn.tasks.parse_model |
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<br><br> |
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## yaml_model_load |
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### ::: ultralytics.nn.tasks.yaml_model_load |
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## guess_model_scale |
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### ::: ultralytics.nn.tasks.guess_model_scale |
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## guess_model_task |
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### ::: ultralytics.nn.tasks.guess_model_task |
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