`ultralytics 8.0.53` DDP AMP and Edge TPU fixes (#1362)
Co-authored-by: Richard Aljaste <richardaljasteabramson@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Vuong Kha Sieu <75152429+hotfur@users.noreply.github.com>pull/1241/head^2 v8.0.53
Glenn Jocher
2 years ago
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GitHub
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46 changed files with 1048 additions and 387 deletions
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YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings |
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and hyperparameters can affect the model's behavior at various stages of the model development process, including |
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training, validation, and prediction. |
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|
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YOLOv8 'yolo' CLI commands use the following syntax: |
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|
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!!! example "" |
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|
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=== "CLI" |
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|
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```bash |
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yolo TASK MODE ARGS |
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``` |
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|
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Where: |
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|
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- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess |
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the `TASK` from the model type. |
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- `MODE` (required) is one of `[train, val, predict, export]` |
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- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults. |
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For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml` |
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GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml). |
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|
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#### Tasks |
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|
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YOLO models can be used for a variety of tasks, including detection, segmentation, and classification. These tasks |
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differ in the type of output they produce and the specific problem they are designed to solve. |
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|
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- **Detect**: Detection tasks involve identifying and localizing objects or regions of interest in an image or video. |
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YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an |
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image. |
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- **Segment**: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to |
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different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for |
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each pixel in an image. |
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- **Classify**: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO |
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models can be used for image classification tasks by predicting the class label of an input image. |
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|
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#### Modes |
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|
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YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes |
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include train, val, and predict. |
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|
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- **Train**: The train mode is used to train the model on a dataset. This mode is typically used during the development |
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and |
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testing phase of a model. |
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- **Val**: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used |
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to |
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tune the model's hyperparameters and detect overfitting. |
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- **Predict**: The predict mode is used to make predictions with the model on new data. This mode is typically used in |
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production or when deploying the model to users. |
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|
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| Key | Value | Description | |
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|--------|----------|-----------------------------------------------------------------------------------------------| |
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| task | 'detect' | inference task, i.e. detect, segment, or classify | |
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| mode | 'train' | YOLO mode, i.e. train, val, predict, or export | |
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| resume | False | resume training from last checkpoint or custom checkpoint if passed as resume=path/to/best.pt | |
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| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml | |
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| data | null | path to data file, i.e. coco128.yaml | |
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|
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### Training |
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|
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Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a |
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dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings |
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include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process |
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include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It |
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is important to carefully tune and experiment with these settings to achieve the best possible performance for a given |
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task. |
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|
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| Key | Value | Description | |
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|-----------------|--------|--------------------------------------------------------------------------------| |
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| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml | |
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| data | null | path to data file, i.e. coco128.yaml | |
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| epochs | 100 | number of epochs to train for | |
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| patience | 50 | epochs to wait for no observable improvement for early stopping of training | |
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| batch | 16 | number of images per batch (-1 for AutoBatch) | |
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| imgsz | 640 | size of input images as integer or w,h | |
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| save | True | save train checkpoints and predict results | |
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| save_period | -1 | Save checkpoint every x epochs (disabled if < 1) | |
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| cache | False | True/ram, disk or False. Use cache for data loading | |
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| device | null | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu | |
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| workers | 8 | number of worker threads for data loading (per RANK if DDP) | |
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| project | null | project name | |
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| name | null | experiment name | |
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| exist_ok | False | whether to overwrite existing experiment | |
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| pretrained | False | whether to use a pretrained model | |
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| optimizer | 'SGD' | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] | |
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| verbose | False | whether to print verbose output | |
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| seed | 0 | random seed for reproducibility | |
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| deterministic | True | whether to enable deterministic mode | |
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| single_cls | False | train multi-class data as single-class | |
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| image_weights | False | use weighted image selection for training | |
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| rect | False | support rectangular training | |
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| cos_lr | False | use cosine learning rate scheduler | |
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| close_mosaic | 10 | disable mosaic augmentation for final 10 epochs | |
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| resume | False | resume training from last checkpoint | |
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| lr0 | 0.01 | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) | |
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| lrf | 0.01 | final learning rate (lr0 * lrf) | |
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| momentum | 0.937 | SGD momentum/Adam beta1 | |
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| weight_decay | 0.0005 | optimizer weight decay 5e-4 | |
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| warmup_epochs | 3.0 | warmup epochs (fractions ok) | |
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| warmup_momentum | 0.8 | warmup initial momentum | |
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| warmup_bias_lr | 0.1 | warmup initial bias lr | |
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| box | 7.5 | box loss gain | |
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| cls | 0.5 | cls loss gain (scale with pixels) | |
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| dfl | 1.5 | dfl loss gain | |
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| fl_gamma | 0.0 | focal loss gamma (efficientDet default gamma=1.5) | |
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| label_smoothing | 0.0 | label smoothing (fraction) | |
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| nbs | 64 | nominal batch size | |
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| overlap_mask | True | masks should overlap during training (segment train only) | |
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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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|
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### Prediction |
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|
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Prediction settings for YOLO models refer to the various hyperparameters and configurations used to make predictions |
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with the model on new data. These settings can affect the model's performance, speed, and accuracy. Some common YOLO |
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prediction settings include the confidence threshold, non-maximum suppression (NMS) threshold, and the number of classes |
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to consider. Other factors that may affect the prediction process include the size and format of the input data, the |
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presence of additional features such as masks or multiple labels per box, and the specific task the model is being used |
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for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a |
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given task. |
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|
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| Key | Value | Description | |
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|----------------|----------------------|----------------------------------------------------------| |
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| source | 'ultralytics/assets' | source directory for images or videos | |
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| conf | 0.25 | object confidence threshold for detection | |
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| iou | 0.7 | intersection over union (IoU) threshold for NMS | |
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| half | False | use half precision (FP16) | |
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| device | null | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
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| show | False | show results if possible | |
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| save | False | save images with results | |
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| save_txt | False | save results as .txt file | |
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| save_conf | False | save results with confidence scores | |
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| save_crop | False | save cropped images with results | |
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| hide_labels | False | hide labels | |
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| hide_conf | False | hide confidence scores | |
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| max_det | 300 | maximum number of detections per image | |
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| vid_stride | False | video frame-rate stride | |
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| line_thickness | 3 | bounding box thickness (pixels) | |
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| visualize | False | visualize model features | |
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| augment | False | apply image augmentation to prediction sources | |
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| agnostic_nms | False | class-agnostic NMS | |
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| retina_masks | False | use high-resolution segmentation masks | |
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| classes | null | filter results by class, i.e. class=0, or class=[0,2,3] | |
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| box | True | Show boxes in segmentation predictions | |
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|
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### Validation |
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|
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Validation settings for YOLO models refer to the various hyperparameters and configurations used to |
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evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and |
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accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed |
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during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation |
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process include the size and composition of the validation dataset and the specific task the model is being used for. It |
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is important to carefully tune and experiment with these settings to ensure that the model is performing well on the |
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validation dataset and to detect and prevent overfitting. |
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|
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| Key | Value | Description | |
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|-------------|-------|--------------------------------------------------------------------| |
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| save_json | False | save results to JSON file | |
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| save_hybrid | False | save hybrid version of labels (labels + additional predictions) | |
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| conf | 0.001 | object confidence threshold for detection | |
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| iou | 0.6 | intersection over union (IoU) threshold for NMS | |
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| max_det | 300 | maximum number of detections per image | |
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| half | True | use half precision (FP16) | |
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| device | null | 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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| rect | False | support rectangular evaluation | |
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| split | val | dataset split to use for validation, i.e. 'val', 'test' or 'train' | |
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|
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### Export |
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|
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Export settings for YOLO models refer to the various configurations and options used to save or |
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export the model for use in other environments or platforms. These settings can affect the model's performance, size, |
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and compatibility with different systems. Some common YOLO export settings include the format of the exported model |
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file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of |
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additional features such as masks or multiple labels per box. Other factors that may affect the export process include |
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the specific task the model is being used for and the requirements or constraints of the target environment or platform. |
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It is important to carefully consider and configure these settings to ensure that the exported model is optimized for |
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the intended use case and can be used effectively in the target environment. |
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|
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### Augmentation |
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|
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Augmentation settings for YOLO models refer to the various transformations and modifications |
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applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's |
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performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the |
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transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each |
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transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other |
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factors that may affect the augmentation process include the size and composition of the original dataset and the |
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specific task the model is being used for. It is important to carefully tune and experiment with these settings to |
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ensure that the augmented dataset is diverse and representative enough to train a high-performing model. |
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|
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| Key | Value | Description | |
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|-------------|-------|-------------------------------------------------| |
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| hsv_h | 0.015 | image HSV-Hue augmentation (fraction) | |
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| hsv_s | 0.7 | image HSV-Saturation augmentation (fraction) | |
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| hsv_v | 0.4 | image HSV-Value augmentation (fraction) | |
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| degrees | 0.0 | image rotation (+/- deg) | |
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| translate | 0.1 | image translation (+/- fraction) | |
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| scale | 0.5 | image scale (+/- gain) | |
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| shear | 0.0 | image shear (+/- deg) | |
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| perspective | 0.0 | image perspective (+/- fraction), range 0-0.001 | |
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| flipud | 0.0 | image flip up-down (probability) | |
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| fliplr | 0.5 | image flip left-right (probability) | |
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| mosaic | 1.0 | image mosaic (probability) | |
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| mixup | 0.0 | image mixup (probability) | |
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| copy_paste | 0.0 | segment copy-paste (probability) | |
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|
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### Logging, checkpoints, plotting and file management |
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|
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Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model. |
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|
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- Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and |
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diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log |
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messages to a file. |
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- Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows |
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you to resume training from a previous point if the training process is interrupted or if you want to experiment with |
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different training configurations. |
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- Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is |
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behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by |
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generating plots using a logging library such as TensorBoard. |
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- File management: Managing the various files generated during the training process, such as model checkpoints, log |
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files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of |
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these files and make it easy to access and analyze them as needed. |
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|
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Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make |
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it easier to debug and optimize the training process. |
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|
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| Key | Value | Description | |
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|----------|--------|------------------------------------------------------------------------------------------------| |
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| project | 'runs' | project name | |
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| name | 'exp' | experiment name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... | |
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| exist_ok | False | whether to overwrite existing experiment | |
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| plots | False | save plots during train/val | |
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| save | False | save train checkpoints and predict results | |
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@ -0,0 +1,65 @@ |
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
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|
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**Benchmark mode** is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks |
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provide information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation) |
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or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export |
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formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for |
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their specific use case based on their requirements for speed and accuracy. |
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|
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!!! tip "Tip" |
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|
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* Export to ONNX or OpenVINO for up to 3x CPU speedup. |
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* Export to TensorRT for up to 5x GPU speedup. |
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|
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## Usage Examples |
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|
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Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a |
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full list of export arguments. |
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|
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!!! example "" |
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|
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=== "Python" |
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|
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```python |
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from ultralytics.yolo.utils.benchmarks import benchmark |
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|
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# Benchmark |
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benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0) |
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``` |
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=== "CLI" |
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|
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```bash |
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yolo benchmark model=yolov8n.pt imgsz=640 half=False device=0 |
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``` |
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|
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## Arguments |
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|
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Arguments such as `model`, `imgsz`, `half`, `device`, and `hard_fail` provide users with the flexibility to fine-tune |
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the benchmarks to their specific needs and compare the performance of different export formats with ease. |
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|
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| Key | Value | Description | |
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|-------------|---------|----------------------------------------------------------------------| |
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| `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml | |
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| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `half` | `False` | FP16 quantization | |
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| `device` | `None` | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu | |
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| `hard_fail` | `False` | do not continue on error (bool), or val floor threshold (float) | |
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|
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## Export Formats |
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|
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Benchmarks will attempt to run automatically on all possible export formats below. |
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|
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| Format | `format` Argument | Model | Metadata | |
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|--------------------------------------------------------------------|-------------------|---------------------------|----------| |
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | |
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@ -0,0 +1,81 @@ |
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
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|
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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 |
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model is converted to a format that can be used by other software applications or hardware devices. This mode is useful |
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when deploying the model to production environments. |
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|
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!!! tip "Tip" |
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|
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* Export to ONNX or OpenVINO for up to 3x CPU speedup. |
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* Export to TensorRT for up to 5x GPU speedup. |
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|
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## Usage Examples |
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|
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Export a YOLOv8n model to a different format like ONNX or TensorRT. See Arguments section below for a full list of |
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export arguments. |
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|
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!!! example "" |
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|
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=== "Python" |
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|
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```python |
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from ultralytics import YOLO |
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|
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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 trained |
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|
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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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|
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```bash |
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yolo export model=yolov8n.pt format=onnx # export official model |
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yolo export model=path/to/best.pt format=onnx # export custom trained model |
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``` |
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|
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## Arguments |
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|
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Export settings for YOLO models refer to the various configurations and options used to save or |
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export the model for use in other environments or platforms. These settings can affect the model's performance, size, |
||||
and compatibility with different systems. Some common YOLO export settings include the format of the exported model |
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file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of |
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additional features such as masks or multiple labels per box. Other factors that may affect the export process include |
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the specific task the model is being used for and the requirements or constraints of the target environment or platform. |
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It is important to carefully consider and configure these settings to ensure that the exported model is optimized for |
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the intended use case and can be used effectively in the target environment. |
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|
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| Key | Value | Description | |
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|-------------|-----------------|------------------------------------------------------| |
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| `format` | `'torchscript'` | format to export to | |
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| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `keras` | `False` | use Keras for TF SavedModel export | |
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| `optimize` | `False` | TorchScript: optimize for mobile | |
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| `half` | `False` | FP16 quantization | |
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| `int8` | `False` | INT8 quantization | |
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| `dynamic` | `False` | ONNX/TF/TensorRT: dynamic axes | |
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| `simplify` | `False` | ONNX: simplify model | |
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| `opset` | `None` | ONNX: opset version (optional, defaults to latest) | |
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| `workspace` | `4` | TensorRT: workspace size (GB) | |
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| `nms` | `False` | CoreML: add NMS | |
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|
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## Export Formats |
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|
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Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, |
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i.e. `format='onnx'` or `format='engine'`. |
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|
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| Format | `format` Argument | Model | Metadata | |
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|--------------------------------------------------------------------|-------------------|---------------------------|----------| |
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | |
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| [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` | ✅ | |
||||
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | |
||||
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | |
||||
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | |
||||
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | |
||||
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | |
||||
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | |
||||
@ -0,0 +1,62 @@ |
||||
# YOLOv8 Modes |
||||
|
||||
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
||||
|
||||
Ultralytics YOLOv8 supports several **modes** that can be used to perform different tasks. These modes are: |
||||
|
||||
**Train**: For training a YOLOv8 model on a custom dataset. |
||||
**Val**: For validating a YOLOv8 model after it has been trained. |
||||
**Predict**: For making predictions using a trained YOLOv8 model on new images or videos. |
||||
**Export**: For exporting a YOLOv8 model to a format that can be used for deployment. |
||||
**Track**: For tracking objects in real-time using a YOLOv8 model. |
||||
**Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy. |
||||
|
||||
## [Train](train.md) |
||||
|
||||
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. |
||||
|
||||
[Train Examples](train.md){ .md-button .md-button--primary} |
||||
|
||||
## [Val](val.md) |
||||
|
||||
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. |
||||
|
||||
[Val Examples](val.md){ .md-button .md-button--primary} |
||||
|
||||
## [Predict](predict.md) |
||||
|
||||
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. |
||||
|
||||
[Predict Examples](predict.md){ .md-button .md-button--primary} |
||||
|
||||
## [Export](export.md) |
||||
|
||||
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. |
||||
|
||||
[Export Examples](export.md){ .md-button .md-button--primary} |
||||
|
||||
## [Track](track.md) |
||||
|
||||
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. |
||||
|
||||
[Track Examples](track.md){ .md-button .md-button--primary} |
||||
|
||||
## [Benchmark](benchmark.md) |
||||
|
||||
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 and segmentation) |
||||
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. |
||||
|
||||
[Benchmark Examples](benchmark.md){ .md-button .md-button--primary} |
||||
@ -0,0 +1,88 @@ |
||||
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
||||
|
||||
**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. |
||||
|
||||
!!! tip "Tip" |
||||
|
||||
* YOLOv8 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml` |
||||
|
||||
## Usage Examples |
||||
|
||||
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Arguments section below for a full list of |
||||
training arguments. |
||||
|
||||
!!! example "" |
||||
|
||||
=== "Python" |
||||
|
||||
```python |
||||
from ultralytics import YOLO |
||||
|
||||
# Load a model |
||||
model = YOLO("yolov8n.yaml") # build a new model from scratch |
||||
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) |
||||
|
||||
# Train the model |
||||
model.train(data="coco128.yaml", epochs=100, imgsz=640) |
||||
``` |
||||
=== "CLI" |
||||
|
||||
```bash |
||||
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 |
||||
``` |
||||
|
||||
## Arguments |
||||
|
||||
Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a |
||||
dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings |
||||
include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process |
||||
include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It |
||||
is important to carefully tune and experiment with these settings to achieve the best possible performance for a given |
||||
task. |
||||
|
||||
| Key | Value | Description | |
||||
|-------------------|----------|-----------------------------------------------------------------------------| |
||||
| `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml | |
||||
| `data` | `None` | path to data file, i.e. coco128.yaml | |
||||
| `epochs` | `100` | number of epochs to train for | |
||||
| `patience` | `50` | epochs to wait for no observable improvement for early stopping of training | |
||||
| `batch` | `16` | number of images per batch (-1 for AutoBatch) | |
||||
| `imgsz` | `640` | size of input images as integer or w,h | |
||||
| `save` | `True` | save train checkpoints and predict results | |
||||
| `save_period` | `-1` | Save checkpoint every x epochs (disabled if < 1) | |
||||
| `cache` | `False` | True/ram, disk or False. Use cache for data loading | |
||||
| `device` | `None` | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu | |
||||
| `workers` | `8` | number of worker threads for data loading (per RANK if DDP) | |
||||
| `project` | `None` | project name | |
||||
| `name` | `None` | experiment name | |
||||
| `exist_ok` | `False` | whether to overwrite existing experiment | |
||||
| `pretrained` | `False` | whether to use a pretrained model | |
||||
| `optimizer` | `'SGD'` | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] | |
||||
| `verbose` | `False` | whether to print verbose output | |
||||
| `seed` | `0` | random seed for reproducibility | |
||||
| `deterministic` | `True` | whether to enable deterministic mode | |
||||
| `single_cls` | `False` | train multi-class data as single-class | |
||||
| `image_weights` | `False` | use weighted image selection for training | |
||||
| `rect` | `False` | support rectangular training | |
||||
| `cos_lr` | `False` | use cosine learning rate scheduler | |
||||
| `close_mosaic` | `10` | disable mosaic augmentation for final 10 epochs | |
||||
| `resume` | `False` | resume training from last checkpoint | |
||||
| `lr0` | `0.01` | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) | |
||||
| `lrf` | `0.01` | final learning rate (lr0 * lrf) | |
||||
| `momentum` | `0.937` | SGD momentum/Adam beta1 | |
||||
| `weight_decay` | `0.0005` | optimizer weight decay 5e-4 | |
||||
| `warmup_epochs` | `3.0` | warmup epochs (fractions ok) | |
||||
| `warmup_momentum` | `0.8` | warmup initial momentum | |
||||
| `warmup_bias_lr` | `0.1` | warmup initial bias lr | |
||||
| `box` | `7.5` | box loss gain | |
||||
| `cls` | `0.5` | cls loss gain (scale with pixels) | |
||||
| `dfl` | `1.5` | dfl loss gain | |
||||
| `fl_gamma` | `0.0` | focal loss gamma (efficientDet default gamma=1.5) | |
||||
| `label_smoothing` | `0.0` | label smoothing (fraction) | |
||||
| `nbs` | `64` | nominal batch size | |
||||
| `overlap_mask` | `True` | masks should overlap during training (segment train only) | |
||||
| `mask_ratio` | `4` | mask downsample ratio (segment train only) | |
||||
| `dropout` | `0.0` | use dropout regularization (classify train only) | |
||||
| `val` | `True` | validate/test during training | |
||||
@ -0,0 +1,86 @@ |
||||
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
||||
|
||||
**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. |
||||
|
||||
!!! tip "Tip" |
||||
|
||||
* YOLOv8 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolov8n.pt` or `model('yolov8n.pt').val()` |
||||
|
||||
## Usage Examples |
||||
|
||||
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's |
||||
training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments. |
||||
|
||||
!!! 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 |
||||
|
||||
# Validate the model |
||||
metrics = model.val() # no arguments needed, dataset and settings remembered |
||||
metrics.box.map # map50-95 |
||||
metrics.box.map50 # map50 |
||||
metrics.box.map75 # map75 |
||||
metrics.box.maps # a list contains map50-95 of each category |
||||
``` |
||||
=== "CLI" |
||||
|
||||
```bash |
||||
yolo detect val model=yolov8n.pt # val official model |
||||
yolo detect val model=path/to/best.pt # val custom model |
||||
``` |
||||
|
||||
## Arguments |
||||
|
||||
Validation settings for YOLO models refer to the various hyperparameters and configurations used to |
||||
evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and |
||||
accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed |
||||
during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation |
||||
process include the size and composition of the validation dataset and the specific task the model is being used for. It |
||||
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the |
||||
validation dataset and to detect and prevent overfitting. |
||||
|
||||
| Key | Value | Description | |
||||
|---------------|---------|--------------------------------------------------------------------| |
||||
| `data` | `None` | path to data file, i.e. coco128.yaml | |
||||
| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
||||
| `batch` | `16` | number of images per batch (-1 for AutoBatch) | |
||||
| `save_json` | `False` | save results to JSON file | |
||||
| `save_hybrid` | `False` | save hybrid version of labels (labels + additional predictions) | |
||||
| `conf` | `0.001` | object confidence threshold for detection | |
||||
| `iou` | `0.6` | intersection over union (IoU) threshold for NMS | |
||||
| `max_det` | `300` | maximum number of detections per image | |
||||
| `half` | `True` | use half precision (FP16) | |
||||
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
||||
| `dnn` | `False` | use OpenCV DNN for ONNX inference | |
||||
| `plots` | `False` | show plots during training | |
||||
| `rect` | `False` | support rectangular evaluation | |
||||
| `split` | `val` | dataset split to use for validation, i.e. 'val', 'test' or 'train' | |
||||
|
||||
## Export Formats |
||||
|
||||
Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, |
||||
i.e. `format='onnx'` or `format='engine'`. |
||||
|
||||
| Format | `format` Argument | Model | Metadata | |
||||
|--------------------------------------------------------------------|-------------------|---------------------------|----------| |
||||
| [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` | ✅ | |
||||
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | |
||||
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | |
||||
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | |
||||
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | |
||||
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | |
||||
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | |
||||
@ -0,0 +1,46 @@ |
||||
# Ultralytics YOLOv8 Tasks |
||||
|
||||
YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to |
||||
perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md), |
||||
and [keypoints](keypoints.md) detection. Each of these tasks has a different objective and use case. |
||||
|
||||
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png"> |
||||
|
||||
## [Detection](detect.md) |
||||
|
||||
Detection is the primary task supported by YOLOv8. It involves detecting objects in an image or video frame and drawing |
||||
bounding boxes around them. The detected objects are classified into different categories based on their features. |
||||
YOLOv8 can detect multiple objects in a single image or video frame with high accuracy and speed. |
||||
|
||||
[Detection Examples](detect.md){ .md-button .md-button--primary} |
||||
|
||||
## [Segmentation](segment.md) |
||||
|
||||
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each |
||||
region is assigned a label based on its content. This task is useful in applications such as image segmentation and |
||||
medical imaging. YOLOv8 uses a variant of the U-Net architecture to perform segmentation. |
||||
|
||||
[Segmentation Examples](segment.md){ .md-button .md-button--primary} |
||||
|
||||
## [Classification](classify.md) |
||||
|
||||
Classification is a task that involves classifying an image into different categories. YOLOv8 can be used to classify |
||||
images based on their content. It uses a variant of the EfficientNet architecture to perform classification. |
||||
|
||||
[Classification Examples](classify.md){ .md-button .md-button--primary} |
||||
|
||||
<!-- |
||||
## [Keypoints](keypoints.md) |
||||
|
||||
Keypoints detection is a task that involves detecting specific points in an image or video frame. These points are |
||||
referred to as keypoints and are used to track movement or pose estimation. YOLOv8 can detect keypoints in an image or |
||||
video frame with high accuracy and speed. |
||||
|
||||
[Keypoints Examples](keypoints.md){ .md-button .md-button--primary} |
||||
--> |
||||
|
||||
## Conclusion |
||||
|
||||
YOLOv8 supports multiple tasks, including detection, segmentation, classification, and keypoints detection. Each of |
||||
these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose |
||||
the appropriate task for your computer vision application. |
||||
@ -0,0 +1,141 @@ |
||||
Key Point Estimation is a task that involves identifying the location of specific points in an image, usually referred |
||||
to as keypoints. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive |
||||
features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]` |
||||
coordinates. |
||||
|
||||
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png"> |
||||
|
||||
The output of a keypoint detector is a set of points that represent the keypoints on the object in the image, usually |
||||
along with the confidence scores for each point. Keypoint estimation is a good choice when you need to identify specific |
||||
parts of an object in a scene, and their location in relation to each other. |
||||
|
||||
!!! tip "Tip" |
||||
|
||||
YOLOv8 _keypoints_ models use the `-kpts` suffix, i.e. `yolov8n-kpts.pt`. These models are trained on the COCO dataset and are suitable for a variety of keypoint estimation tasks. |
||||
|
||||
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary} |
||||
|
||||
## Train TODO |
||||
|
||||
Train an OpenPose model on a custom dataset of keypoints using the OpenPose framework. For more information on how to |
||||
train an OpenPose model on a custom dataset, see the OpenPose Training page. |
||||
|
||||
!!! example "" |
||||
|
||||
=== "Python" |
||||
|
||||
```python |
||||
from ultralytics import YOLO |
||||
|
||||
# Load a model |
||||
model = YOLO("yolov8n.yaml") # build a new model from scratch |
||||
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) |
||||
|
||||
# Train the model |
||||
model.train(data="coco128.yaml", epochs=100, imgsz=640) |
||||
``` |
||||
=== "CLI" |
||||
|
||||
```bash |
||||
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 |
||||
``` |
||||
|
||||
## Val TODO |
||||
|
||||
Validate trained YOLOv8n model accuracy on the COCO128 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.pt") # load an official model |
||||
model = YOLO("path/to/best.pt") # load a custom model |
||||
|
||||
# Validate the model |
||||
metrics = model.val() # no arguments needed, dataset and settings remembered |
||||
metrics.box.map # map50-95 |
||||
metrics.box.map50 # map50 |
||||
metrics.box.map75 # map75 |
||||
metrics.box.maps # a list contains map50-95 of each category |
||||
``` |
||||
=== "CLI" |
||||
|
||||
```bash |
||||
yolo detect val model=yolov8n.pt # val official model |
||||
yolo detect val model=path/to/best.pt # val custom model |
||||
``` |
||||
|
||||
## Predict TODO |
||||
|
||||
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 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 |
||||
``` |
||||
|
||||
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page. |
||||
|
||||
## Export TODO |
||||
|
||||
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 export model=yolov8n.pt format=onnx # export official model |
||||
yolo export model=path/to/best.pt format=onnx # export custom trained model |
||||
``` |
||||
|
||||
Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models, |
||||
i.e. `yolo predict model=yolov8n-pose.onnx`. |
||||
|
||||
| Format | `format` Argument | Model | Metadata | |
||||
|--------------------------------------------------------------------|-------------------|---------------------------|----------| |
||||
| [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` | ✅ | |
||||
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | |
||||
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | |
||||
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | |
||||
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | |
||||
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | |
||||
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | |
||||
@ -0,0 +1,250 @@ |
||||
YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings |
||||
and hyperparameters can affect the model's behavior at various stages of the model development process, including |
||||
training, validation, and prediction. |
||||
|
||||
YOLOv8 'yolo' CLI commands use the following syntax: |
||||
|
||||
!!! example "" |
||||
|
||||
=== "CLI" |
||||
|
||||
```bash |
||||
yolo TASK MODE ARGS |
||||
``` |
||||
|
||||
Where: |
||||
|
||||
- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess |
||||
the `TASK` from the model type. |
||||
- `MODE` (required) is one of `[train, val, predict, export]` |
||||
- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults. |
||||
For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml` |
||||
GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml). |
||||
|
||||
#### Tasks |
||||
|
||||
YOLO models can be used for a variety of tasks, including detection, segmentation, and classification. These tasks |
||||
differ in the type of output they produce and the specific problem they are designed to solve. |
||||
|
||||
- **Detect**: Detection tasks involve identifying and localizing objects or regions of interest in an image or video. |
||||
YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an |
||||
image. |
||||
- **Segment**: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to |
||||
different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for |
||||
each pixel in an image. |
||||
- **Classify**: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO |
||||
models can be used for image classification tasks by predicting the class label of an input image. |
||||
|
||||
#### Modes |
||||
|
||||
YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes |
||||
include train, val, and predict. |
||||
|
||||
- **Train**: The train mode is used to train the model on a dataset. This mode is typically used during the development |
||||
and |
||||
testing phase of a model. |
||||
- **Val**: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used |
||||
to |
||||
tune the model's hyperparameters and detect overfitting. |
||||
- **Predict**: The predict mode is used to make predictions with the model on new data. This mode is typically used in |
||||
production or when deploying the model to users. |
||||
|
||||
| Key | Value | Description | |
||||
|----------|------------|-----------------------------------------------------------------------------------------------| |
||||
| `task` | `'detect'` | inference task, i.e. detect, segment, or classify | |
||||
| `mode` | `'train'` | YOLO mode, i.e. train, val, predict, or export | |
||||
| `resume` | `False` | resume training from last checkpoint or custom checkpoint if passed as resume=path/to/best.pt | |
||||
| `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml | |
||||
| `data` | `None` | path to data file, i.e. coco128.yaml | |
||||
|
||||
### Training |
||||
|
||||
Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a |
||||
dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings |
||||
include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process |
||||
include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It |
||||
is important to carefully tune and experiment with these settings to achieve the best possible performance for a given |
||||
task. |
||||
|
||||
| Key | Value | Description | |
||||
|-------------------|----------|-----------------------------------------------------------------------------| |
||||
| `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml | |
||||
| `data` | `None` | path to data file, i.e. coco128.yaml | |
||||
| `epochs` | `100` | number of epochs to train for | |
||||
| `patience` | `50` | epochs to wait for no observable improvement for early stopping of training | |
||||
| `batch` | `16` | number of images per batch (-1 for AutoBatch) | |
||||
| `imgsz` | `640` | size of input images as integer or w,h | |
||||
| `save` | `True` | save train checkpoints and predict results | |
||||
| `save_period` | `-1` | Save checkpoint every x epochs (disabled if < 1) | |
||||
| `cache` | `False` | True/ram, disk or False. Use cache for data loading | |
||||
| `device` | `None` | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu | |
||||
| `workers` | `8` | number of worker threads for data loading (per RANK if DDP) | |
||||
| `project` | `None` | project name | |
||||
| `name` | `None` | experiment name | |
||||
| `exist_ok` | `False` | whether to overwrite existing experiment | |
||||
| `pretrained` | `False` | whether to use a pretrained model | |
||||
| `optimizer` | `'SGD'` | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] | |
||||
| `verbose` | `False` | whether to print verbose output | |
||||
| `seed` | `0` | random seed for reproducibility | |
||||
| `deterministic` | `True` | whether to enable deterministic mode | |
||||
| `single_cls` | `False` | train multi-class data as single-class | |
||||
| `image_weights` | `False` | use weighted image selection for training | |
||||
| `rect` | `False` | support rectangular training | |
||||
| `cos_lr` | `False` | use cosine learning rate scheduler | |
||||
| `close_mosaic` | `10` | disable mosaic augmentation for final 10 epochs | |
||||
| `resume` | `False` | resume training from last checkpoint | |
||||
| `lr0` | `0.01` | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) | |
||||
| `lrf` | `0.01` | final learning rate (lr0 * lrf) | |
||||
| `momentum` | `0.937` | SGD momentum/Adam beta1 | |
||||
| `weight_decay` | `0.0005` | optimizer weight decay 5e-4 | |
||||
| `warmup_epochs` | `3.0` | warmup epochs (fractions ok) | |
||||
| `warmup_momentum` | `0.8` | warmup initial momentum | |
||||
| `warmup_bias_lr` | `0.1` | warmup initial bias lr | |
||||
| `box` | `7.5` | box loss gain | |
||||
| `cls` | `0.5` | cls loss gain (scale with pixels) | |
||||
| `dfl` | `1.5` | dfl loss gain | |
||||
| `fl_gamma` | `0.0` | focal loss gamma (efficientDet default gamma=1.5) | |
||||
| `label_smoothing` | `0.0` | label smoothing (fraction) | |
||||
| `nbs` | `64` | nominal batch size | |
||||
| `overlap_mask` | `True` | masks should overlap during training (segment train only) | |
||||
| `mask_ratio` | `4` | mask downsample ratio (segment train only) | |
||||
| `dropout` | `0.0` | use dropout regularization (classify train only) | |
||||
| `val` | `True` | validate/test during training | |
||||
|
||||
### Prediction |
||||
|
||||
Prediction settings for YOLO models refer to the various hyperparameters and configurations used to make predictions |
||||
with the model on new data. These settings can affect the model's performance, speed, and accuracy. Some common YOLO |
||||
prediction settings include the confidence threshold, non-maximum suppression (NMS) threshold, and the number of classes |
||||
to consider. Other factors that may affect the prediction process include the size and format of the input data, the |
||||
presence of additional features such as masks or multiple labels per box, and the specific task the model is being used |
||||
for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a |
||||
given task. |
||||
|
||||
| Key | Value | Description | |
||||
|------------------|------------------------|----------------------------------------------------------| |
||||
| `source` | `'ultralytics/assets'` | source directory for images or videos | |
||||
| `conf` | `0.25` | object confidence threshold for detection | |
||||
| `iou` | `0.7` | intersection over union (IoU) threshold for NMS | |
||||
| `half` | `False` | use half precision (FP16) | |
||||
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
||||
| `show` | `False` | show results if possible | |
||||
| `save` | `False` | save images with results | |
||||
| `save_txt` | `False` | save results as .txt file | |
||||
| `save_conf` | `False` | save results with confidence scores | |
||||
| `save_crop` | `False` | save cropped images with results | |
||||
| `hide_labels` | `False` | hide labels | |
||||
| `hide_conf` | `False` | hide confidence scores | |
||||
| `max_det` | `300` | maximum number of detections per image | |
||||
| `vid_stride` | `False` | video frame-rate stride | |
||||
| `line_thickness` | `3` | bounding box thickness (pixels) | |
||||
| `visualize` | `False` | visualize model features | |
||||
| `augment` | `False` | apply image augmentation to prediction sources | |
||||
| `agnostic_nms` | `False` | class-agnostic NMS | |
||||
| `retina_masks` | `False` | use high-resolution segmentation masks | |
||||
| `classes` | `None` | filter results by class, i.e. class=0, or class=[0,2,3] | |
||||
| `box` | `True` | Show boxes in segmentation predictions | |
||||
|
||||
### Validation |
||||
|
||||
Validation settings for YOLO models refer to the various hyperparameters and configurations used to |
||||
evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and |
||||
accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed |
||||
during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation |
||||
process include the size and composition of the validation dataset and the specific task the model is being used for. It |
||||
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the |
||||
validation dataset and to detect and prevent overfitting. |
||||
|
||||
| Key | Value | Description | |
||||
|---------------|---------|--------------------------------------------------------------------| |
||||
| `save_json` | `False` | save results to JSON file | |
||||
| `save_hybrid` | `False` | save hybrid version of labels (labels + additional predictions) | |
||||
| `conf` | `0.001` | object confidence threshold for detection | |
||||
| `iou` | `0.6` | intersection over union (IoU) threshold for NMS | |
||||
| `max_det` | `300` | maximum number of detections per image | |
||||
| `half` | `True` | use half precision (FP16) | |
||||
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
||||
| `dnn` | `False` | use OpenCV DNN for ONNX inference | |
||||
| `plots` | `False` | show plots during training | |
||||
| `rect` | `False` | support rectangular evaluation | |
||||
| `split` | `val` | dataset split to use for validation, i.e. 'val', 'test' or 'train' | |
||||
|
||||
### Export |
||||
|
||||
Export settings for YOLO models refer to the various configurations and options used to save or |
||||
export the model for use in other environments or platforms. These settings can affect the model's performance, size, |
||||
and compatibility with different systems. Some common YOLO export settings include the format of the exported model |
||||
file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of |
||||
additional features such as masks or multiple labels per box. Other factors that may affect the export process include |
||||
the specific task the model is being used for and the requirements or constraints of the target environment or platform. |
||||
It is important to carefully consider and configure these settings to ensure that the exported model is optimized for |
||||
the intended use case and can be used effectively in the target environment. |
||||
|
||||
| Key | Value | Description | |
||||
|-------------|-----------------|------------------------------------------------------| |
||||
| `format` | `'torchscript'` | format to export to | |
||||
| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
||||
| `keras` | `False` | use Keras for TF SavedModel export | |
||||
| `optimize` | `False` | TorchScript: optimize for mobile | |
||||
| `half` | `False` | FP16 quantization | |
||||
| `int8` | `False` | INT8 quantization | |
||||
| `dynamic` | `False` | ONNX/TF/TensorRT: dynamic axes | |
||||
| `simplify` | `False` | ONNX: simplify model | |
||||
| `opset` | `None` | ONNX: opset version (optional, defaults to latest) | |
||||
| `workspace` | `4` | TensorRT: workspace size (GB) | |
||||
| `nms` | `False` | CoreML: add NMS | |
||||
|
||||
### Augmentation |
||||
|
||||
Augmentation settings for YOLO models refer to the various transformations and modifications |
||||
applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's |
||||
performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the |
||||
transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each |
||||
transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other |
||||
factors that may affect the augmentation process include the size and composition of the original dataset and the |
||||
specific task the model is being used for. It is important to carefully tune and experiment with these settings to |
||||
ensure that the augmented dataset is diverse and representative enough to train a high-performing model. |
||||
|
||||
| Key | Value | Description | |
||||
|---------------|-------|-------------------------------------------------| |
||||
| `hsv_h` | 0.015 | image HSV-Hue augmentation (fraction) | |
||||
| `hsv_s` | 0.7 | image HSV-Saturation augmentation (fraction) | |
||||
| `hsv_v` | 0.4 | image HSV-Value augmentation (fraction) | |
||||
| `degrees` | 0.0 | image rotation (+/- deg) | |
||||
| `translate` | 0.1 | image translation (+/- fraction) | |
||||
| `scale` | 0.5 | image scale (+/- gain) | |
||||
| `shear` | 0.0 | image shear (+/- deg) | |
||||
| `perspective` | 0.0 | image perspective (+/- fraction), range 0-0.001 | |
||||
| `flipud` | 0.0 | image flip up-down (probability) | |
||||
| `fliplr` | 0.5 | image flip left-right (probability) | |
||||
| `mosaic` | 1.0 | image mosaic (probability) | |
||||
| `mixup` | 0.0 | image mixup (probability) | |
||||
| `copy_paste` | 0.0 | segment copy-paste (probability) | |
||||
|
||||
### Logging, checkpoints, plotting and file management |
||||
|
||||
Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model. |
||||
|
||||
- Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and |
||||
diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log |
||||
messages to a file. |
||||
- Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows |
||||
you to resume training from a previous point if the training process is interrupted or if you want to experiment with |
||||
different training configurations. |
||||
- Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is |
||||
behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by |
||||
generating plots using a logging library such as TensorBoard. |
||||
- File management: Managing the various files generated during the training process, such as model checkpoints, log |
||||
files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of |
||||
these files and make it easy to access and analyze them as needed. |
||||
|
||||
Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make |
||||
it easier to debug and optimize the training process. |
||||
|
||||
| Key | Value | Description | |
||||
|------------|----------|------------------------------------------------------------------------------------------------| |
||||
| `project` | `'runs'` | project name | |
||||
| `name` | `'exp'` | experiment name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... | |
||||
| `exist_ok` | `False` | whether to overwrite existing experiment | |
||||
| `plots` | `False` | save plots during train/val | |
||||
| `save` | `False` | save train checkpoints and predict results | |
||||
Loading…
Reference in new issue