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ultralytics 8.0.54
TFLite export improvements and fixes (#1447)
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@ -79,7 +79,7 @@ pip install ultralytics
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YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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```bash
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```bash
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yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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```
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`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
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`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
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@ -71,7 +71,7 @@ pip install ultralytics
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YOLOv8 可以直接在命令行界面(CLI)中使用 `yolo` 命令运行:
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YOLOv8 可以直接在命令行界面(CLI)中使用 `yolo` 命令运行:
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```bash
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```bash
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yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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```
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`yolo`可以用于各种任务和模式,并接受额外的参数,例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)
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`yolo`可以用于各种任务和模式,并接受额外的参数,例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)
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@ -22,11 +22,11 @@ export arguments.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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# Export the model
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model.export(format="onnx")
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model.export(format='onnx')
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```
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```
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=== "CLI"
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=== "CLI"
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@ -26,9 +26,9 @@ Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official detection model
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model = YOLO('yolov8n.pt') # load an official detection model
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model = YOLO("yolov8n-seg.pt") # load an official segmentation model
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model = YOLO('yolov8n-seg.pt') # load an official segmentation model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Track with the model
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# Track with the model
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
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@ -60,7 +60,7 @@ to [predict page](https://docs.ultralytics.com/modes/predict/).
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```python
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```python
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from ultralytics import YOLO
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from ultralytics import YOLO
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model = YOLO("yolov8n.pt")
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model = YOLO('yolov8n.pt')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
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```
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```
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=== "CLI"
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=== "CLI"
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@ -82,7 +82,7 @@ any configurations(expect the `tracker_type`) you need to.
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```python
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```python
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from ultralytics import YOLO
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from ultralytics import YOLO
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model = YOLO("yolov8n.pt")
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model = YOLO('yolov8n.pt')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml')
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```
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```
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=== "CLI"
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=== "CLI"
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@ -21,16 +21,24 @@ training arguments.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO('yolov8n.yaml') # build a new model from YAML
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
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# Train the model
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# Train the model
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model.train(data="coco128.yaml", epochs=100, imgsz=640)
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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```
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=== "CLI"
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=== "CLI"
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```bash
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```bash
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# Build a new model from YAML and start training from scratch
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yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
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# Start training from a pretrained *.pt model
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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# Build a new model from YAML, transfer pretrained weights to it and start training
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yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
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```
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```
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## Arguments
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## Arguments
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@ -21,8 +21,8 @@ training `data` and arguments as model attributes. See Arguments section below f
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -60,14 +60,14 @@ classification into their Python projects using YOLOv8.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO('yolov8n.yaml') # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Use the model
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# Use the model
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results = model.train(data="coco128.yaml", epochs=3) # train the model
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results = model.train(data='coco128.yaml', epochs=3) # train the model
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results = model.val() # evaluate model performance on the validation set
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results = model.val() # evaluate model performance on the validation set
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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success = model.export(format="onnx") # export the model to ONNX format
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success = model.export(format='onnx') # export the model to ONNX format
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```
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```
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[Python Guide](usage/python.md){.md-button .md-button--primary}
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[Python Guide](usage/python.md){.md-button .md-button--primary}
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@ -26,11 +26,11 @@ see the [Configuration](../usage/cfg.md) page.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n-cls.yaml") # build a new model from scratch
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model = YOLO('yolov8n-cls.yaml') # build a new model from scratch
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model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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# Train the model
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model.train(data="mnist160", epochs=100, imgsz=64)
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model.train(data='mnist160', epochs=100, imgsz=64)
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```
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```
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=== "CLI"
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=== "CLI"
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@ -51,8 +51,8 @@ it's training `data` and arguments as model attributes.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n-cls.pt") # load an official model
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model = YOLO('yolov8n-cls.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -78,17 +78,17 @@ Use a trained YOLOv8n-cls model to run predictions on images.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n-cls.pt") # load an official model
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model = YOLO('yolov8n-cls.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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```
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=== "CLI"
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=== "CLI"
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```bash
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```bash
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yolo classify predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo classify predict model=yolov8n-cls.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo classify predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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yolo classify predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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@ -105,11 +105,11 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n-cls.pt") # load an official model
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model = YOLO('yolov8n-cls.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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# Export the model
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model.export(format="onnx")
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model.export(format='onnx')
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```
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```
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=== "CLI"
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=== "CLI"
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@ -26,11 +26,11 @@ the [Configuration](../usage/cfg.md) page.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO('yolov8n.yaml') # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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# Train the model
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model.train(data="coco128.yaml", epochs=100, imgsz=640)
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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```
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=== "CLI"
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=== "CLI"
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@ -51,8 +51,8 @@ training `data` and arguments as model attributes.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -80,17 +80,17 @@ Use a trained YOLOv8n model to run predictions on images.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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```
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=== "CLI"
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=== "CLI"
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```bash
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```bash
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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@ -107,11 +107,11 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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# Export the model
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model.export(format="onnx")
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model.export(format='onnx')
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```
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```
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=== "CLI"
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=== "CLI"
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@ -28,11 +28,11 @@ train an OpenPose model on a custom dataset, see the OpenPose Training page.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO('yolov8n.yaml') # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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# Train the model
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model.train(data="coco128.yaml", epochs=100, imgsz=640)
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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```
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=== "CLI"
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=== "CLI"
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@ -53,8 +53,8 @@ training `data` and arguments as model attributes.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -82,17 +82,17 @@ Use a trained YOLOv8n model to run predictions on images.
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from ultralytics import YOLO
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from ultralytics import YOLO
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# Load a model
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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```
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=== "CLI"
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=== "CLI"
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```bash
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```bash
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
|
||||||
```
|
```
|
||||||
|
|
||||||
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
|
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
|
||||||
@ -109,11 +109,11 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
|
|||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
# Load a model
|
# Load a model
|
||||||
model = YOLO("yolov8n.pt") # load an official model
|
model = YOLO('yolov8n.pt') # load an official model
|
||||||
model = YOLO("path/to/best.pt") # load a custom trained
|
model = YOLO('path/to/best.pt') # load a custom trained
|
||||||
|
|
||||||
# Export the model
|
# Export the model
|
||||||
model.export(format="onnx")
|
model.export(format='onnx')
|
||||||
```
|
```
|
||||||
=== "CLI"
|
=== "CLI"
|
||||||
|
|
||||||
|
@ -26,11 +26,11 @@ arguments see the [Configuration](../usage/cfg.md) page.
|
|||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
# Load a model
|
# Load a model
|
||||||
model = YOLO("yolov8n-seg.yaml") # build a new model from scratch
|
model = YOLO('yolov8n-seg.yaml') # build a new model from scratch
|
||||||
model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
|
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
|
||||||
|
|
||||||
# Train the model
|
# Train the model
|
||||||
model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
|
model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
|
||||||
```
|
```
|
||||||
=== "CLI"
|
=== "CLI"
|
||||||
|
|
||||||
@ -51,8 +51,8 @@ retains it's training `data` and arguments as model attributes.
|
|||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
# Load a model
|
# Load a model
|
||||||
model = YOLO("yolov8n-seg.pt") # load an official model
|
model = YOLO('yolov8n-seg.pt') # load an official model
|
||||||
model = YOLO("path/to/best.pt") # load a custom model
|
model = YOLO('path/to/best.pt') # load a custom model
|
||||||
|
|
||||||
# Validate the model
|
# Validate the model
|
||||||
metrics = model.val() # no arguments needed, dataset and settings remembered
|
metrics = model.val() # no arguments needed, dataset and settings remembered
|
||||||
@ -84,17 +84,17 @@ Use a trained YOLOv8n-seg model to run predictions on images.
|
|||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
# Load a model
|
# Load a model
|
||||||
model = YOLO("yolov8n-seg.pt") # load an official model
|
model = YOLO('yolov8n-seg.pt') # load an official model
|
||||||
model = YOLO("path/to/best.pt") # load a custom model
|
model = YOLO('path/to/best.pt') # load a custom model
|
||||||
|
|
||||||
# Predict with the model
|
# Predict with the model
|
||||||
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
|
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
|
||||||
```
|
```
|
||||||
=== "CLI"
|
=== "CLI"
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
|
yolo segment predict model=yolov8n-seg.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
|
||||||
yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
yolo segment predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
|
||||||
```
|
```
|
||||||
|
|
||||||
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
|
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
|
||||||
@ -111,11 +111,11 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
|
|||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
# Load a model
|
# Load a model
|
||||||
model = YOLO("yolov8n-seg.pt") # load an official model
|
model = YOLO('yolov8n-seg.pt') # load an official model
|
||||||
model = YOLO("path/to/best.pt") # load a custom trained
|
model = YOLO('path/to/best.pt') # load a custom trained
|
||||||
|
|
||||||
# Export the model
|
# Export the model
|
||||||
model.export(format="onnx")
|
model.export(format='onnx')
|
||||||
```
|
```
|
||||||
=== "CLI"
|
=== "CLI"
|
||||||
|
|
||||||
|
@ -17,7 +17,7 @@ def on_predict_batch_end(predictor):
|
|||||||
im0s = im0s if isinstance(im0s, list) else [im0s]
|
im0s = im0s if isinstance(im0s, list) else [im0s]
|
||||||
predictor.results = zip(predictor.results, im0s)
|
predictor.results = zip(predictor.results, im0s)
|
||||||
|
|
||||||
model = YOLO(f"yolov8n.pt")
|
model = YOLO(f'yolov8n.pt')
|
||||||
model.add_callback("on_predict_batch_end", on_predict_batch_end)
|
model.add_callback("on_predict_batch_end", on_predict_batch_end)
|
||||||
for (result, frame) in model.track/predict():
|
for (result, frame) in model.track/predict():
|
||||||
pass
|
pass
|
||||||
|
@ -59,8 +59,8 @@ Use a trained YOLOv8n model to run predictions on images.
|
|||||||
!!! example ""
|
!!! example ""
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
|
yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
|
||||||
yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
|
||||||
```
|
```
|
||||||
|
|
||||||
## Export
|
## Export
|
||||||
|
@ -6,7 +6,7 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
|||||||
```python
|
```python
|
||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
model = YOLO("yolov8n.pt") # pass any model type
|
model = YOLO('yolov8n.pt') # pass any model type
|
||||||
model.train(epochs=5)
|
model.train(epochs=5)
|
||||||
```
|
```
|
||||||
|
|
||||||
@ -14,8 +14,8 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
|||||||
```python
|
```python
|
||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
model = YOLO("yolov8n.yaml")
|
model = YOLO('yolov8n.yaml')
|
||||||
model.train(data="coco128.yaml", epochs=5)
|
model.train(data='coco128.yaml', epochs=5)
|
||||||
```
|
```
|
||||||
|
|
||||||
=== "Resume"
|
=== "Resume"
|
||||||
@ -31,8 +31,8 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
|||||||
```python
|
```python
|
||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
model = YOLO("yolov8n.yaml")
|
model = YOLO('yolov8n.yaml')
|
||||||
model.train(data="coco128.yaml", epochs=5)
|
model.train(data='coco128.yaml', epochs=5)
|
||||||
model.val() # It'll automatically evaluate the data you trained.
|
model.val() # It'll automatically evaluate the data you trained.
|
||||||
```
|
```
|
||||||
|
|
||||||
@ -44,7 +44,7 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
|||||||
# It'll use the data yaml file in model.pt if you don't set data.
|
# It'll use the data yaml file in model.pt if you don't set data.
|
||||||
model.val()
|
model.val()
|
||||||
# or you can set the data you want to val
|
# or you can set the data you want to val
|
||||||
model.val(data="coco128.yaml")
|
model.val(data='coco128.yaml')
|
||||||
```
|
```
|
||||||
|
|
||||||
!!! example "Predict"
|
!!! example "Predict"
|
||||||
|
@ -3,7 +3,7 @@
|
|||||||
|
|
||||||
# Base ----------------------------------------
|
# Base ----------------------------------------
|
||||||
matplotlib>=3.2.2
|
matplotlib>=3.2.2
|
||||||
numpy>=1.18.5
|
numpy>=1.21.6
|
||||||
opencv-python>=4.6.0
|
opencv-python>=4.6.0
|
||||||
Pillow>=7.1.2
|
Pillow>=7.1.2
|
||||||
PyYAML>=5.3.1
|
PyYAML>=5.3.1
|
||||||
|
@ -207,9 +207,9 @@ def test_predict_callback_and_setup():
|
|||||||
def test_result():
|
def test_result():
|
||||||
model = YOLO('yolov8n-seg.pt')
|
model = YOLO('yolov8n-seg.pt')
|
||||||
res = model([SOURCE, SOURCE])
|
res = model([SOURCE, SOURCE])
|
||||||
res[0].cpu().numpy()
|
|
||||||
res[0].plot(show_conf=False)
|
res[0].plot(show_conf=False)
|
||||||
print(res[0].path)
|
res[0] = res[0].cpu().numpy()
|
||||||
|
print(res[0].path, res[0].masks.masks)
|
||||||
|
|
||||||
model = YOLO('yolov8n.pt')
|
model = YOLO('yolov8n.pt')
|
||||||
res = model(SOURCE)
|
res = model(SOURCE)
|
||||||
|
@ -1,6 +1,6 @@
|
|||||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||||
|
|
||||||
__version__ = '8.0.53'
|
__version__ = '8.0.54'
|
||||||
|
|
||||||
from ultralytics.yolo.engine.model import YOLO
|
from ultralytics.yolo.engine.model import YOLO
|
||||||
from ultralytics.yolo.utils.checks import check_yolo as checks
|
from ultralytics.yolo.utils.checks import check_yolo as checks
|
||||||
|
@ -411,12 +411,12 @@ class Detect(nn.Module):
|
|||||||
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
|
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
|
||||||
self.shape = shape
|
self.shape = shape
|
||||||
|
|
||||||
if self.export and self.format == 'edgetpu': # FlexSplitV ops issue
|
x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
|
||||||
x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
|
if self.export and self.format in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs'): # avoid TF FlexSplitV ops
|
||||||
box = x_cat[:, :self.reg_max * 4]
|
box = x_cat[:, :self.reg_max * 4]
|
||||||
cls = x_cat[:, self.reg_max * 4:]
|
cls = x_cat[:, self.reg_max * 4:]
|
||||||
else:
|
else:
|
||||||
box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
|
box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
|
||||||
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
|
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
|
||||||
y = torch.cat((dbox, cls.sigmoid()), 1)
|
y = torch.cat((dbox, cls.sigmoid()), 1)
|
||||||
return y if self.export else (y, x)
|
return y if self.export else (y, x)
|
||||||
|
@ -11,8 +11,8 @@ import torch.nn as nn
|
|||||||
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
|
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
|
||||||
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
|
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
|
||||||
GhostBottleneck, GhostConv, Segment)
|
GhostBottleneck, GhostConv, Segment)
|
||||||
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
|
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load
|
||||||
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
|
from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_yaml
|
||||||
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
|
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
|
||||||
intersect_dicts, make_divisible, model_info, scale_img, time_sync)
|
intersect_dicts, make_divisible, model_info, scale_img, time_sync)
|
||||||
|
|
||||||
@ -151,15 +151,19 @@ class BaseModel(nn.Module):
|
|||||||
m.strides = fn(m.strides)
|
m.strides = fn(m.strides)
|
||||||
return self
|
return self
|
||||||
|
|
||||||
def load(self, weights):
|
def load(self, weights, verbose=True):
|
||||||
"""
|
"""Load the weights into the model.
|
||||||
This function loads the weights of the model from a file
|
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
weights (str): The weights to load into the model.
|
weights (dict) or (torch.nn.Module): The pre-trained weights to be loaded.
|
||||||
|
verbose (bool, optional): Whether to log the transfer progress. Defaults to True.
|
||||||
"""
|
"""
|
||||||
# Force all tasks to implement this function
|
model = weights['model'] if isinstance(weights, dict) else weights # torchvision models are not dicts
|
||||||
raise NotImplementedError('This function needs to be implemented by derived classes!')
|
csd = model.float().state_dict() # checkpoint state_dict as FP32
|
||||||
|
csd = intersect_dicts(csd, self.state_dict()) # intersect
|
||||||
|
self.load_state_dict(csd, strict=False) # load
|
||||||
|
if verbose:
|
||||||
|
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
|
||||||
|
|
||||||
|
|
||||||
class DetectionModel(BaseModel):
|
class DetectionModel(BaseModel):
|
||||||
@ -234,13 +238,6 @@ class DetectionModel(BaseModel):
|
|||||||
y[-1] = y[-1][..., i:] # small
|
y[-1] = y[-1][..., i:] # small
|
||||||
return y
|
return y
|
||||||
|
|
||||||
def load(self, weights, verbose=True):
|
|
||||||
csd = weights.float().state_dict() # checkpoint state_dict as FP32
|
|
||||||
csd = intersect_dicts(csd, self.state_dict()) # intersect
|
|
||||||
self.load_state_dict(csd, strict=False) # load
|
|
||||||
if verbose and RANK == -1:
|
|
||||||
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
|
|
||||||
|
|
||||||
|
|
||||||
class SegmentationModel(DetectionModel):
|
class SegmentationModel(DetectionModel):
|
||||||
# YOLOv8 segmentation model
|
# YOLOv8 segmentation model
|
||||||
@ -293,12 +290,6 @@ class ClassificationModel(BaseModel):
|
|||||||
self.names = {i: f'{i}' for i in range(self.yaml['nc'])} # default names dict
|
self.names = {i: f'{i}' for i in range(self.yaml['nc'])} # default names dict
|
||||||
self.info()
|
self.info()
|
||||||
|
|
||||||
def load(self, weights):
|
|
||||||
model = weights['model'] if isinstance(weights, dict) else weights # torchvision models are not dicts
|
|
||||||
csd = model.float().state_dict()
|
|
||||||
csd = intersect_dicts(csd, self.state_dict()) # intersect
|
|
||||||
self.load_state_dict(csd, strict=False) # load
|
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def reshape_outputs(model, nc):
|
def reshape_outputs(model, nc):
|
||||||
# Update a TorchVision classification model to class count 'n' if required
|
# Update a TorchVision classification model to class count 'n' if required
|
||||||
@ -338,6 +329,7 @@ def torch_safe_load(weight):
|
|||||||
"""
|
"""
|
||||||
from ultralytics.yolo.utils.downloads import attempt_download_asset
|
from ultralytics.yolo.utils.downloads import attempt_download_asset
|
||||||
|
|
||||||
|
check_suffix(file=weight, suffix='.pt')
|
||||||
file = attempt_download_asset(weight) # search online if missing locally
|
file = attempt_download_asset(weight) # search online if missing locally
|
||||||
try:
|
try:
|
||||||
return torch.load(file, map_location='cpu'), file # load
|
return torch.load(file, map_location='cpu'), file # load
|
||||||
|
@ -54,11 +54,10 @@ CFG_FRACTION_KEYS = ('dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay',
|
|||||||
'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou') # fractional floats limited to 0.0 - 1.0
|
'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou') # fractional floats limited to 0.0 - 1.0
|
||||||
CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
|
CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
|
||||||
'line_thickness', 'workspace', 'nbs', 'save_period')
|
'line_thickness', 'workspace', 'nbs', 'save_period')
|
||||||
CFG_BOOL_KEYS = ('save', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect',
|
CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect', 'cos_lr',
|
||||||
'cos_lr', 'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show',
|
'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt',
|
||||||
'save_txt', 'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment',
|
'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms',
|
||||||
'agnostic_nms', 'retina_masks', 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms',
|
'retina_masks', 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader')
|
||||||
'v5loader')
|
|
||||||
|
|
||||||
# Define valid tasks and modes
|
# Define valid tasks and modes
|
||||||
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
|
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
|
||||||
@ -290,6 +289,8 @@ def entrypoint(debug=''):
|
|||||||
from ultralytics.yolo.engine.model import YOLO
|
from ultralytics.yolo.engine.model import YOLO
|
||||||
overrides['model'] = model
|
overrides['model'] = model
|
||||||
model = YOLO(model, task=task)
|
model = YOLO(model, task=task)
|
||||||
|
if isinstance(overrides.get('pretrained'), str):
|
||||||
|
model.load(overrides['pretrained'])
|
||||||
|
|
||||||
# Task Update
|
# Task Update
|
||||||
if task != model.task:
|
if task != model.task:
|
||||||
|
@ -188,7 +188,7 @@ class Exporter:
|
|||||||
m.dynamic = self.args.dynamic
|
m.dynamic = self.args.dynamic
|
||||||
m.export = True
|
m.export = True
|
||||||
m.format = self.args.format
|
m.format = self.args.format
|
||||||
elif isinstance(m, C2f) and not edgetpu:
|
elif isinstance(m, C2f) and not any((saved_model, pb, tflite, edgetpu, tfjs)):
|
||||||
# EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
|
# EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
|
||||||
m.forward = m.forward_split
|
m.forward = m.forward_split
|
||||||
|
|
||||||
|
@ -8,8 +8,8 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
|
|||||||
guess_model_task, nn)
|
guess_model_task, nn)
|
||||||
from ultralytics.yolo.cfg import get_cfg
|
from ultralytics.yolo.cfg import get_cfg
|
||||||
from ultralytics.yolo.engine.exporter import Exporter
|
from ultralytics.yolo.engine.exporter import Exporter
|
||||||
from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, ONLINE, RANK, ROOT,
|
from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
|
||||||
callbacks, is_git_dir, is_pip_package, yaml_load)
|
is_git_dir, yaml_load)
|
||||||
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
|
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
|
||||||
from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
|
from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
|
||||||
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
|
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
|
||||||
@ -153,16 +153,10 @@ class YOLO:
|
|||||||
f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
|
f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
|
||||||
f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
|
f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
|
||||||
|
|
||||||
def _check_pip_update(self):
|
@smart_inference_mode()
|
||||||
|
def reset_weights(self):
|
||||||
"""
|
"""
|
||||||
Inform user of ultralytics package update availability
|
Resets the model modules parameters to randomly initialized values, losing all training information.
|
||||||
"""
|
|
||||||
if ONLINE and is_pip_package():
|
|
||||||
check_pip_update_available()
|
|
||||||
|
|
||||||
def reset(self):
|
|
||||||
"""
|
|
||||||
Resets the model modules.
|
|
||||||
"""
|
"""
|
||||||
self._check_is_pytorch_model()
|
self._check_is_pytorch_model()
|
||||||
for m in self.model.modules():
|
for m in self.model.modules():
|
||||||
@ -170,6 +164,18 @@ class YOLO:
|
|||||||
m.reset_parameters()
|
m.reset_parameters()
|
||||||
for p in self.model.parameters():
|
for p in self.model.parameters():
|
||||||
p.requires_grad = True
|
p.requires_grad = True
|
||||||
|
return self
|
||||||
|
|
||||||
|
@smart_inference_mode()
|
||||||
|
def load(self, weights='yolov8n.pt'):
|
||||||
|
"""
|
||||||
|
Transfers parameters with matching names and shapes from 'weights' to model.
|
||||||
|
"""
|
||||||
|
self._check_is_pytorch_model()
|
||||||
|
if isinstance(weights, (str, Path)):
|
||||||
|
weights, self.ckpt = attempt_load_one_weight(weights)
|
||||||
|
self.model.load(weights)
|
||||||
|
return self
|
||||||
|
|
||||||
def info(self, verbose=False):
|
def info(self, verbose=False):
|
||||||
"""
|
"""
|
||||||
@ -299,7 +305,7 @@ class YOLO:
|
|||||||
**kwargs (Any): Any number of arguments representing the training configuration.
|
**kwargs (Any): Any number of arguments representing the training configuration.
|
||||||
"""
|
"""
|
||||||
self._check_is_pytorch_model()
|
self._check_is_pytorch_model()
|
||||||
self._check_pip_update()
|
check_pip_update_available()
|
||||||
overrides = self.overrides.copy()
|
overrides = self.overrides.copy()
|
||||||
overrides.update(kwargs)
|
overrides.update(kwargs)
|
||||||
if kwargs.get('cfg'):
|
if kwargs.get('cfg'):
|
||||||
|
@ -48,7 +48,7 @@ class Results:
|
|||||||
self.probs = probs if probs is not None else None
|
self.probs = probs if probs is not None else None
|
||||||
self.names = names
|
self.names = names
|
||||||
self.path = path
|
self.path = path
|
||||||
self._keys = [k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None]
|
self._keys = ('boxes', 'masks', 'probs')
|
||||||
|
|
||||||
def pandas(self):
|
def pandas(self):
|
||||||
pass
|
pass
|
||||||
@ -56,7 +56,7 @@ class Results:
|
|||||||
|
|
||||||
def __getitem__(self, idx):
|
def __getitem__(self, idx):
|
||||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||||
for k in self._keys:
|
for k in self.keys:
|
||||||
setattr(r, k, getattr(self, k)[idx])
|
setattr(r, k, getattr(self, k)[idx])
|
||||||
return r
|
return r
|
||||||
|
|
||||||
@ -70,30 +70,30 @@ class Results:
|
|||||||
|
|
||||||
def cpu(self):
|
def cpu(self):
|
||||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||||
for k in self._keys:
|
for k in self.keys:
|
||||||
setattr(r, k, getattr(self, k).cpu())
|
setattr(r, k, getattr(self, k).cpu())
|
||||||
return r
|
return r
|
||||||
|
|
||||||
def numpy(self):
|
def numpy(self):
|
||||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||||
for k in self._keys:
|
for k in self.keys:
|
||||||
setattr(r, k, getattr(self, k).numpy())
|
setattr(r, k, getattr(self, k).numpy())
|
||||||
return r
|
return r
|
||||||
|
|
||||||
def cuda(self):
|
def cuda(self):
|
||||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||||
for k in self._keys:
|
for k in self.keys:
|
||||||
setattr(r, k, getattr(self, k).cuda())
|
setattr(r, k, getattr(self, k).cuda())
|
||||||
return r
|
return r
|
||||||
|
|
||||||
def to(self, *args, **kwargs):
|
def to(self, *args, **kwargs):
|
||||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||||
for k in self._keys:
|
for k in self.keys:
|
||||||
setattr(r, k, getattr(self, k).to(*args, **kwargs))
|
setattr(r, k, getattr(self, k).to(*args, **kwargs))
|
||||||
return r
|
return r
|
||||||
|
|
||||||
def __len__(self):
|
def __len__(self):
|
||||||
for k in self._keys:
|
for k in self.keys:
|
||||||
return len(getattr(self, k))
|
return len(getattr(self, k))
|
||||||
|
|
||||||
def __str__(self):
|
def __str__(self):
|
||||||
@ -107,6 +107,10 @@ class Results:
|
|||||||
name = self.__class__.__name__
|
name = self.__class__.__name__
|
||||||
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
|
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
|
||||||
|
|
||||||
|
@property
|
||||||
|
def keys(self):
|
||||||
|
return [k for k in self._keys if getattr(self, k) is not None]
|
||||||
|
|
||||||
def plot(self, show_conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
|
def plot(self, show_conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
|
||||||
"""
|
"""
|
||||||
Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.
|
Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.
|
||||||
|
@ -46,14 +46,14 @@ HELP_MSG = \
|
|||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
|
|
||||||
# Load a model
|
# Load a model
|
||||||
model = YOLO("yolov8n.yaml") # build a new model from scratch
|
model = YOLO('yolov8n.yaml') # build a new model from scratch
|
||||||
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
|
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
|
||||||
|
|
||||||
# Use the model
|
# Use the model
|
||||||
results = model.train(data="coco128.yaml", epochs=3) # train the model
|
results = model.train(data="coco128.yaml", epochs=3) # train the model
|
||||||
results = model.val() # evaluate model performance on the validation set
|
results = model.val() # evaluate model performance on the validation set
|
||||||
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
|
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
|
||||||
success = model.export(format="onnx") # export the model to ONNX format
|
success = model.export(format='onnx') # export the model to ONNX format
|
||||||
|
|
||||||
3. Use the command line interface (CLI):
|
3. Use the command line interface (CLI):
|
||||||
|
|
||||||
|
@ -1,6 +1,6 @@
|
|||||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||||
"""
|
"""
|
||||||
AutoBatch utils
|
Functions for estimating the best YOLO batch size to use a fraction of the available CUDA memory in PyTorch.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
from copy import deepcopy
|
from copy import deepcopy
|
||||||
@ -13,18 +13,35 @@ from ultralytics.yolo.utils.torch_utils import profile
|
|||||||
|
|
||||||
|
|
||||||
def check_train_batch_size(model, imgsz=640, amp=True):
|
def check_train_batch_size(model, imgsz=640, amp=True):
|
||||||
# Check YOLOv5 training batch size
|
"""
|
||||||
|
Check YOLO training batch size using the autobatch() function.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model (torch.nn.Module): YOLO model to check batch size for.
|
||||||
|
imgsz (int): Image size used for training.
|
||||||
|
amp (bool): If True, use automatic mixed precision (AMP) for training.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
int: Optimal batch size computed using the autobatch() function.
|
||||||
|
"""
|
||||||
|
|
||||||
with torch.cuda.amp.autocast(amp):
|
with torch.cuda.amp.autocast(amp):
|
||||||
return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
|
return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
|
||||||
|
|
||||||
|
|
||||||
def autobatch(model, imgsz=640, fraction=0.7, batch_size=16):
|
def autobatch(model, imgsz=640, fraction=0.67, batch_size=16):
|
||||||
# Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory
|
"""
|
||||||
# Usage:
|
Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory.
|
||||||
# import torch
|
|
||||||
# from utils.autobatch import autobatch
|
Args:
|
||||||
# model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
|
model: YOLO model to compute batch size for.
|
||||||
# print(autobatch(model))
|
imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640.
|
||||||
|
fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.67.
|
||||||
|
batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
int: The optimal batch size.
|
||||||
|
"""
|
||||||
|
|
||||||
# Check device
|
# Check device
|
||||||
prefix = colorstr('AutoBatch: ')
|
prefix = colorstr('AutoBatch: ')
|
||||||
|
@ -1,5 +1,5 @@
|
|||||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
|
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING, colorstr
|
||||||
|
|
||||||
try:
|
try:
|
||||||
from torch.utils.tensorboard import SummaryWriter
|
from torch.utils.tensorboard import SummaryWriter
|
||||||
@ -18,11 +18,14 @@ def _log_scalars(scalars, step=0):
|
|||||||
|
|
||||||
|
|
||||||
def on_pretrain_routine_start(trainer):
|
def on_pretrain_routine_start(trainer):
|
||||||
global writer
|
if SummaryWriter:
|
||||||
try:
|
try:
|
||||||
writer = SummaryWriter(str(trainer.save_dir))
|
global writer
|
||||||
except Exception as e:
|
writer = SummaryWriter(str(trainer.save_dir))
|
||||||
LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}')
|
prefix = colorstr('TensorBoard: ')
|
||||||
|
LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
|
||||||
|
except Exception as e:
|
||||||
|
LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}')
|
||||||
|
|
||||||
|
|
||||||
def on_batch_end(trainer):
|
def on_batch_end(trainer):
|
||||||
|
@ -20,8 +20,8 @@ import requests
|
|||||||
import torch
|
import torch
|
||||||
from matplotlib import font_manager
|
from matplotlib import font_manager
|
||||||
|
|
||||||
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, emojis,
|
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads,
|
||||||
is_colab, is_docker, is_jupyter, is_online)
|
emojis, is_colab, is_docker, is_jupyter, is_online, is_pip_package)
|
||||||
|
|
||||||
|
|
||||||
def is_ascii(s) -> bool:
|
def is_ascii(s) -> bool:
|
||||||
@ -141,12 +141,14 @@ def check_pip_update_available():
|
|||||||
Returns:
|
Returns:
|
||||||
bool: True if an update is available, False otherwise.
|
bool: True if an update is available, False otherwise.
|
||||||
"""
|
"""
|
||||||
from ultralytics import __version__
|
if ONLINE and is_pip_package():
|
||||||
latest = check_latest_pypi_version()
|
with contextlib.suppress(ConnectionError):
|
||||||
if pkg.parse_version(__version__) < pkg.parse_version(latest): # update is available
|
from ultralytics import __version__
|
||||||
LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
|
latest = check_latest_pypi_version()
|
||||||
f"Update with 'pip install -U ultralytics'")
|
if pkg.parse_version(__version__) < pkg.parse_version(latest): # update is available
|
||||||
return True
|
LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
|
||||||
|
f"Update with 'pip install -U ultralytics'")
|
||||||
|
return True
|
||||||
return False
|
return False
|
||||||
|
|
||||||
|
|
||||||
@ -235,11 +237,11 @@ def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
|
|||||||
# Check file(s) for acceptable suffix
|
# Check file(s) for acceptable suffix
|
||||||
if file and suffix:
|
if file and suffix:
|
||||||
if isinstance(suffix, str):
|
if isinstance(suffix, str):
|
||||||
suffix = [suffix]
|
suffix = (suffix, )
|
||||||
for f in file if isinstance(file, (list, tuple)) else [file]:
|
for f in file if isinstance(file, (list, tuple)) else [file]:
|
||||||
s = Path(f).suffix.lower() # file suffix
|
s = Path(f).suffix.lower() # file suffix
|
||||||
if len(s):
|
if len(s):
|
||||||
assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}'
|
assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}, not {s}'
|
||||||
|
|
||||||
|
|
||||||
def check_yolov5u_filename(file: str, verbose: bool = True):
|
def check_yolov5u_filename(file: str, verbose: bool = True):
|
||||||
|
@ -76,7 +76,7 @@ class DetectionPredictor(BasePredictor):
|
|||||||
if self.args.save_crop:
|
if self.args.save_crop:
|
||||||
save_one_box(d.xyxy,
|
save_one_box(d.xyxy,
|
||||||
imc,
|
imc,
|
||||||
file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',
|
file=self.save_dir / 'crops' / self.model.names[c] / f'{self.data_path.stem}.jpg',
|
||||||
BGR=True)
|
BGR=True)
|
||||||
|
|
||||||
return log_string
|
return log_string
|
||||||
|
@ -58,10 +58,9 @@ class DetectionTrainer(BaseTrainer):
|
|||||||
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
|
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
|
||||||
|
|
||||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||||
model = DetectionModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
|
model = DetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
|
||||||
if weights:
|
if weights:
|
||||||
model.load(weights)
|
model.load(weights)
|
||||||
|
|
||||||
return model
|
return model
|
||||||
|
|
||||||
def get_validator(self):
|
def get_validator(self):
|
||||||
|
@ -90,7 +90,7 @@ class SegmentationPredictor(DetectionPredictor):
|
|||||||
if self.args.save_crop:
|
if self.args.save_crop:
|
||||||
save_one_box(d.xyxy,
|
save_one_box(d.xyxy,
|
||||||
imc,
|
imc,
|
||||||
file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',
|
file=self.save_dir / 'crops' / self.model.names[c] / f'{self.data_path.stem}.jpg',
|
||||||
BGR=True)
|
BGR=True)
|
||||||
|
|
||||||
return log_string
|
return log_string
|
||||||
|
Loading…
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Reference in New Issue
Block a user