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75 lines
1.4 KiB
Markdown
75 lines
1.4 KiB
Markdown
---
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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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---
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## BaseModel
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---
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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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---
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### ::: ultralytics.nn.tasks.SegmentationModel
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<br><br>
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## PoseModel
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---
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### ::: ultralytics.nn.tasks.PoseModel
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<br><br>
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## ClassificationModel
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---
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### ::: ultralytics.nn.tasks.ClassificationModel
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<br><br>
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## RTDETRDetectionModel
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---
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### ::: ultralytics.nn.tasks.RTDETRDetectionModel
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<br><br>
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## Ensemble
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---
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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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---
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### ::: ultralytics.nn.tasks.torch_safe_load
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<br><br>
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## attempt_load_weights
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### ::: ultralytics.nn.tasks.attempt_load_weights
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<br><br>
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## attempt_load_one_weight
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---
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### ::: ultralytics.nn.tasks.attempt_load_one_weight
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<br><br>
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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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<br><br>
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## guess_model_scale
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---
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### ::: ultralytics.nn.tasks.guess_model_scale
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<br><br>
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## guess_model_task
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---
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### ::: ultralytics.nn.tasks.guess_model_task
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<br><br>
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