Release 8.0.5 PR (#279)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Izam Mohammed <106471909+izam-mohammed@users.noreply.github.com> Co-authored-by: Yue WANG 王跃 <92371174+yuewangg@users.noreply.github.com> Co-authored-by: Thibaut Lucas <thibautlucas13@gmail.com>single_channel
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docs.ultralytics.com
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This is the simplest way of simply using YOLOv8 models in a Python environment. It can be imported from
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the `ultralytics` module.
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!!! example "Train"
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=== "From pretrained(recommanded)"
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.pt") # pass any model type
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model.train(epochs=5)
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```
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=== "From scratch"
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml")
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model.train(data="coco128.yaml", epochs=5)
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```
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=== "Resume"
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```python
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TODO: Resume feature is under development and should be released soon.
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```
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!!! example "Val"
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=== "Val after training"
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml")
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model.train(data="coco128.yaml", epochs=5)
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model.val() # It'll automatically evaluate the data you trained.
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```
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=== "Val independently"
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```python
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from ultralytics import YOLO
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model = YOLO("model.pt")
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# It'll use the data yaml file in model.pt if you don't set data.
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model.val()
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# or you can set the data you want to val
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model.val(data="coco128.yaml")
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```
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!!! example "Predict"
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=== "From source"
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```python
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from ultralytics import YOLO
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model = YOLO("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", show=True) # Display preds. Accepts all yolo predict arguments
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```
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=== "From image/ndarray/tensor"
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```python
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# TODO, still working on it.
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```
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=== "Return outputs"
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```python
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from ultralytics import YOLO
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model = YOLO("model.pt")
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outputs = model.predict(source="0", return_outputs=True) # treat predict as a Python generator
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for output in outputs:
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# each output here is a dict.
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# for detection
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print(output["det"]) # np.ndarray, (N, 6), xyxy, score, cls
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# for segmentation
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print(output["det"]) # np.ndarray, (N, 6), xyxy, score, cls
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print(output["segment"]) # List[np.ndarray] * N, bounding coordinates of masks
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# for classify
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print(output["prob"]) # np.ndarray, (num_class, ), cls prob
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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("model.pt")
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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 Reference
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[Model reference](reference/model.md){ .md-button .md-button--primary}
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---
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### Using 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
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from `BaseTrainer`.
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!!! tip "Detection Trainer Example"
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```python
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from ultralytics.yolo import v8 import DetectionTrainer, DetectionValidator, DetectionPredictor
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# trainer
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trainer = DetectionTrainer(overrides={})
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trainer.train()
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trained_model = trainer.best
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# Validator
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val = DetectionValidator(args=...)
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val(model=trained_model)
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# predictor
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pred = DetectionPredictor(overrides={})
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pred(source=SOURCE, model=trained_model)
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# resume from last weight
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overrides["resume"] = trainer.last
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trainer = detect.DetectionTrainer(overrides=overrides)
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```
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You can easily customize Trainers to support custom tasks or explore R&D ideas.
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Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization
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Section.
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[Customization tutorials](engine.md){ .md-button .md-button--primary}
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All task Predictors are inherited from `BasePredictors` class that contains the model validation routine boilerplate. You can override any function of these Trainers to suit your needs.
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All task Predictors are inherited from `BasePredictors` class that contains the model validation routine boilerplate.
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You can override any function of these Trainers to suit your needs.
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---
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### BasePredictor API Reference
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:::ultralytics.yolo.engine.predictor.BasePredictor
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All task Trainers are inherited from `BaseTrainer` class that contains the model training and optimzation routine boilerplate. You can override any function of these Trainers to suit your needs.
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All task Trainers are inherited from `BaseTrainer` class that contains the model training and optimzation routine
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boilerplate. You can override any function of these Trainers to suit your needs.
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---
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### BaseTrainer API Reference
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:::ultralytics.yolo.engine.trainer.BaseTrainer
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All task Validators are inherited from `BaseValidator` class that contains the model validation routine boilerplate. You can override any function of these Trainers to suit your needs.
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All task Validators are inherited from `BaseValidator` class that contains the model validation routine boilerplate. You
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can override any function of these Trainers to suit your needs.
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---
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### BaseValidator API Reference
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:::ultralytics.yolo.engine.validator.BaseValidator
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### Exporter API Reference
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:::ultralytics.yolo.engine.exporter.Exporter
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## 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("yolov8n.yaml")
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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("yolov8n.pt") # pass any model type
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model(...) # inference
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model.train(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("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", show=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("model.pt")
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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 Reference
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[Model reference](reference/model.md){ .md-button .md-button--primary}
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---
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### Using 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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!!! tip "Detection Trainer Example"
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```python
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from ultralytics.yolo import v8 import DetectionTrainer, DetectionValidator, DetectionPredictor
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# trainer
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trainer = DetectionTrainer(overrides={})
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trainer.train()
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trained_model = trainer.best
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# Validator
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val = DetectionValidator(args=...)
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val(model=trained_model)
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# predictor
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pred = DetectionPredictor(overrides={})
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pred(source=SOURCE, model=trained_model)
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# resume from last weight
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overrides["resume"] = trainer.last
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trainer = detect.DetectionTrainer(overrides=overrides)
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```
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You can easily customize Trainers to support custom tasks or explore R&D ideas.
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Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section.
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[Customization tutorials](engine.md){ .md-button .md-button--primary}
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Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of
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predefined classes.
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
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The output of an image classifier is a single class label and a confidence score. Image
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classification is useful when you need to know only what class an image belongs to and don't need to know where objects
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of that class are located or what their exact shape is.
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!!! tip "Tip"
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YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8/cls){.md-button .md-button--primary}
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## Train
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Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments
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see the [Configuration](../config.md) page.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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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.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="mnist160", epochs=100, imgsz=64)
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```
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=== "CLI"
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```bash
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yolo task=classify mode=train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
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```
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## Val
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Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the `model` retains
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it's training `data` and arguments as model attributes.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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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("path/to/best.pt") # load a custom model
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# Validate the model
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results = model.val() # no arguments needed, dataset and settings remembered
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```
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=== "CLI"
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```bash
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yolo task=classify mode=val model=yolov8n-cls.pt # val official model
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yolo task=classify mode=val model=path/to/best.pt # val custom model
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```
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## Predict
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Use a trained YOLOv8n-cls model to run predictions on images.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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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("path/to/best.pt") # load a custom 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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```
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=== "CLI"
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```bash
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yolo task=classify mode=predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo task=classify mode=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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## Export
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Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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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("path/to/best.pt") # load a custom trained
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# Export the model
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model.export(format="onnx")
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```
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=== "CLI"
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```bash
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yolo mode=export model=yolov8n-cls.pt format=onnx # export official model
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yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
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```
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Available YOLOv8-cls export formats include:
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| Format | `format=` | Model |
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|----------------------------------------------------------------------------|---------------|-------------------------------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-cls.torchscript` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-cls_openvino_model/` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-cls.engine` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-cls.mlmodel` |
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| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-cls_saved_model/` |
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| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` |
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| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-cls.tflite` |
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| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` |
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| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` |
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Object detection is a task that involves identifying the location and class of objects in an image or video stream.
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
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The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class
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labels
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and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a
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scene, but don't need to know exactly where the object is or its exact shape.
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!!! tip "Tip"
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YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
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## Train
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Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
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the [Configuration](../config.md) page.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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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.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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yolo task=detect mode=train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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## Val
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Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
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training `data` and arguments as model attributes.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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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("path/to/best.pt") # load a custom model
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# Validate the model
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results = model.val() # no arguments needed, dataset and settings remembered
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```
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=== "CLI"
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```bash
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yolo task=detect mode=val model=yolov8n.pt # val official model
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yolo task=detect mode=val model=path/to/best.pt # val custom model
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```
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## Predict
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||||
|
||||
Use a trained YOLOv8n model to run predictions on images.
|
||||
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n.pt") # load an official model
|
||||
model = YOLO("path/to/best.pt") # load a custom model
|
||||
|
||||
# Predict with the model
|
||||
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=detect mode=predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
|
||||
yolo task=detect mode=predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
||||
```
|
||||
|
||||
## Export
|
||||
|
||||
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
|
||||
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n.pt") # load an official model
|
||||
model = YOLO("path/to/best.pt") # load a custom trained
|
||||
|
||||
# Export the model
|
||||
model.export(format="onnx")
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo mode=export model=yolov8n.pt format=onnx # export official model
|
||||
yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
|
||||
```
|
||||
|
||||
Available YOLOv8 export formats include:
|
||||
|
||||
| Format | `format=` | Model |
|
||||
|----------------------------------------------------------------------------|--------------------|---------------------------|
|
||||
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` |
|
||||
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` |
|
||||
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` |
|
||||
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` |
|
||||
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` |
|
||||
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` |
|
||||
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` |
|
||||
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` |
|
||||
| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` |
|
||||
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` |
|
||||
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` |
|
||||
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` |
|
||||
@ -0,0 +1,135 @@
|
||||
Instance segmentation goes a step further than object detection and involves identifying individual objects in an image
|
||||
and segmenting them from the rest of the image.
|
||||
|
||||
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
|
||||
|
||||
The output of an instance segmentation model is a set of masks or
|
||||
contours that outline each object in the image, along with class labels and confidence scores for each object. Instance
|
||||
segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.
|
||||
|
||||
!!! tip "Tip"
|
||||
|
||||
YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO.
|
||||
|
||||
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8/seg){.md-button .md-button--primary}
|
||||
|
||||
## Train
|
||||
|
||||
Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available
|
||||
arguments see the [Configuration](../config.md) page.
|
||||
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n-seg.yaml") # build a new model from scratch
|
||||
model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=segment mode=train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Val
|
||||
|
||||
Validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the `model`
|
||||
retains it's training `data` and arguments as model attributes.
|
||||
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n-seg.pt") # load an official model
|
||||
model = YOLO("path/to/best.pt") # load a custom model
|
||||
|
||||
# Validate the model
|
||||
results = model.val() # no arguments needed, dataset and settings remembered
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=segment mode=val model=yolov8n-seg.pt # val official model
|
||||
yolo task=segment mode=val model=path/to/best.pt # val custom model
|
||||
```
|
||||
|
||||
## Predict
|
||||
|
||||
Use a trained YOLOv8n-seg model to run predictions on images.
|
||||
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n-seg.pt") # load an official model
|
||||
model = YOLO("path/to/best.pt") # load a custom model
|
||||
|
||||
# Predict with the model
|
||||
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=segment mode=predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
|
||||
yolo task=segment mode=predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
||||
```
|
||||
|
||||
## Export
|
||||
|
||||
Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
|
||||
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n-seg.pt") # load an official model
|
||||
model = YOLO("path/to/best.pt") # load a custom trained
|
||||
|
||||
# Export the model
|
||||
model.export(format="onnx")
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo mode=export model=yolov8n-seg.pt format=onnx # export official model
|
||||
yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
|
||||
```
|
||||
|
||||
Available YOLOv8-seg export formats include:
|
||||
|
||||
| Format | `format=` | Model |
|
||||
|----------------------------------------------------------------------------|---------------|-------------------------------|
|
||||
| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` |
|
||||
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-seg.torchscript` |
|
||||
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-seg.onnx` |
|
||||
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-seg_openvino_model/` |
|
||||
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-seg.engine` |
|
||||
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-seg.mlmodel` |
|
||||
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-seg_saved_model/` |
|
||||
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` |
|
||||
| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-seg.tflite` |
|
||||
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` |
|
||||
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` |
|
||||
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` |
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in new issue