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100 lines
3.1 KiB
100 lines
3.1 KiB
## Using YOLO models |
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This is the simplest way of simply using yolo models in a python environment. It can be imported from the `ultralytics` module. |
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!!! example "Usage" |
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=== "Training" |
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```python |
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from ultralytics import YOLO |
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model = YOLO() |
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model.new("n.yaml") # pass any model type |
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model(img_tensor) # Or model.forward(). inference. |
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model.train(data="coco128.yaml", epochs=5) |
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``` |
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=== "Training pretrained" |
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```python |
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from ultralytics import YOLO |
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model = YOLO() |
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model.load("n.pt") # pass any model type |
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model(...) # inference |
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model.train(data="coco128.yaml", epochs=5) |
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``` |
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=== "Resume Training" |
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```python |
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from ultralytics import YOLO |
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model = YOLO() |
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model.resume(task="detect") # resume last detection training |
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model.resume(model="last.pt") # resume from a given model/run |
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``` |
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=== "Visualize/save Predictions" |
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```python |
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from ultralytics import YOLO |
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model = YOLO() |
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model.load("model.pt") |
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model.predict(source="0") # accepts all formats - img/folder/vid.*(mp4/format). 0 for webcam |
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model.predict(source="folder", view_img=True) # Display preds. Accepts all yolo predict arguments |
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``` |
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!!! note "Export and Deployment" |
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=== "Export, Fuse & info" |
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```python |
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from ultralytics import YOLO |
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model = YOLO() |
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model.fuse() |
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model.info(verbose=True) # Print model information |
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model.export(format=) # TODO: |
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``` |
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=== "Deployment" |
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More functionality coming soon |
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To know more about using `YOLO` models, refer Model class refernce |
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[Model reference](#){ .md-button .md-button--primary} |
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--- |
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### Customizing Tasks with Trainers |
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`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`. |
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You can easily cusotmize Trainers to support custom tasks or explore R&D ideas. |
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!!! tip "Trainer Examples" |
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=== "DetectionTrainer" |
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```python |
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from ultralytics import yolo |
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trainer = yolo.DetectionTrainer(data=..., epochs=1) # override default configs |
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trainer = yolo.DetectionTrainer(data=..., epochs=1, device="1,2,3,4") # DDP |
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trainer.train() |
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``` |
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=== "SegmentationTrainer" |
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```python |
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from ultralytics import yolo |
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trainer = yolo.SegmentationTrainer(data=..., epochs=1) # override default configs |
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trainer = yolo.SegmentationTrainer(data=..., epochs=1, device="0,1,2,3") # DDP |
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trainer.train() |
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``` |
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=== "ClassificationTrainer" |
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```python |
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from ultralytics import yolo |
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trainer = yolo.ClassificationTrainer(data=..., epochs=1) # override default configs |
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trainer = yolo.ClassificationTrainer(data=..., epochs=1, device="0,1,2,3") # DDP |
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trainer.train() |
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``` |
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Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section. More details about the base engine classes is available in the reference section. |
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[Customization tutorials](#){ .md-button .md-button--primary}
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