Update docs predict, buttons, reference (#6585)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -40,35 +40,35 @@ This comprehensive guide aims to give you an overview and practical insights int
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Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
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[Train Examples](train.md){ .md-button .md-button--primary}
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[Train Examples](train.md){ .md-button }
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## [Val](val.md)
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Val mode is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
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[Val Examples](val.md){ .md-button .md-button--primary}
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[Val Examples](val.md){ .md-button }
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## [Predict](predict.md)
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Predict mode is used for making predictions using a trained YOLOv8 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.
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[Predict Examples](predict.md){ .md-button .md-button--primary}
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[Predict Examples](predict.md){ .md-button }
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## [Export](export.md)
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Export mode is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.
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[Export Examples](export.md){ .md-button .md-button--primary}
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[Export Examples](export.md){ .md-button }
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## [Track](track.md)
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Track mode is used for tracking objects in real-time using a YOLOv8 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.
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[Track Examples](track.md){ .md-button .md-button--primary}
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[Track Examples](track.md){ .md-button }
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## [Benchmark](benchmark.md)
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Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation and pose)
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or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
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[Benchmark Examples](benchmark.md){ .md-button .md-button--primary}
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[Benchmark Examples](benchmark.md){ .md-button }
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@ -339,33 +339,38 @@ Below are code examples for using each source type:
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model.predict('bus.jpg', save=True, imgsz=320, conf=0.5)
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```
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All supported arguments:
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Inference arguments:
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| Name | Type | Default | Description |
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|-----------------|----------------|------------------------|--------------------------------------------------------------------------------|
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| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos |
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| `conf` | `float` | `0.25` | object confidence threshold for detection |
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| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS |
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| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
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| `half` | `bool` | `False` | use half precision (FP16) |
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| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
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| `show` | `bool` | `False` | show results if possible |
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| `save` | `bool` | `False` | save images with results |
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| `save_txt` | `bool` | `False` | save results as .txt file |
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| `save_conf` | `bool` | `False` | save results with confidence scores |
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| `save_crop` | `bool` | `False` | save cropped images with results |
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| `hide_labels` | `bool` | `False` | hide labels |
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| `hide_conf` | `bool` | `False` | hide confidence scores |
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| `max_det` | `int` | `300` | maximum number of detections per image |
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| `vid_stride` | `bool` | `False` | video frame-rate stride |
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| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) |
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| `line_width` | `None or int` | `None` | The line width of the bounding boxes. If None, it is scaled to the image size. |
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| `visualize` | `bool` | `False` | visualize model features |
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| `augment` | `bool` | `False` | apply image augmentation to prediction sources |
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| `agnostic_nms` | `bool` | `False` | class-agnostic NMS |
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| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks |
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| `classes` | `None or list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `boxes` | `bool` | `True` | Show boxes in segmentation predictions |
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| Name | Type | Default | Description |
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|-----------------|----------------|------------------------|----------------------------------------------------------------------------|
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| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos |
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| `conf` | `float` | `0.25` | object confidence threshold for detection |
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| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS |
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| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
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| `half` | `bool` | `False` | use half precision (FP16) |
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| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
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| `max_det` | `int` | `300` | maximum number of detections per image |
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| `vid_stride` | `bool` | `False` | video frame-rate stride |
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| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) |
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| `visualize` | `bool` | `False` | visualize model features |
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| `augment` | `bool` | `False` | apply image augmentation to prediction sources |
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| `agnostic_nms` | `bool` | `False` | class-agnostic NMS |
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| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks |
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| `classes` | `None or list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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Visualization arguments:
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| Name | Type | Default | Description |
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|---------------|---------------|---------|-----------------------------------------------------------------|
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| `show` | `bool` | `False` | show predicted images and videos if environment allows |
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| `save` | `bool` | `False` | save predicted images and videos |
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| `save_txt` | `bool` | `False` | save results as `.txt` file |
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| `save_conf` | `bool` | `False` | save results with confidence scores |
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| `save_crop` | `bool` | `False` | save cropped images with results |
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| `show_labels` | `bool` | `True` | show prediction labels, i.e. 'person' |
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| `show_conf` | `bool` | `True` | show prediction confidence, i.e. '0.99' |
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| `show_boxes` | `bool` | `True` | show prediction boxes |
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| `line_width` | `None or int` | `None` | line width of the bounding boxes. Scaled to image size if None. |
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## Image and Video Formats
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@ -223,6 +223,7 @@ Training settings for YOLO models refer to the various hyperparameters and confi
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| `mask_ratio` | `4` | mask downsample ratio (segment train only) |
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| `dropout` | `0.0` | use dropout regularization (classify train only) |
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| `val` | `True` | validate/test during training |
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| `plots` | `False` | save plots and images during train/val |
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## Logging
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@ -80,7 +80,6 @@ Validation settings for YOLO models refer to the various hyperparameters and con
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| `half` | `True` | use half precision (FP16) |
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| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
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| `dnn` | `False` | use OpenCV DNN for ONNX inference |
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| `plots` | `False` | show plots during training |
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| `plots` | `False` | save plots and images during train/val |
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| `rect` | `False` | rectangular val with each batch collated for minimum padding |
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| `split` | `val` | dataset split to use for validation, i.e. 'val', 'test' or 'train' |
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