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109 lines
8.6 KiB
109 lines
8.6 KiB
## Ultralytics YOLO
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Default training settings and hyperparameters for medium-augmentation COCO training
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### Setting the operation type
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???+ note "Operation"
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| Key | Value | Description |
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|--------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify`.<br> - `init` is a special case that creates a copy of default.yaml configs to the current working dir |
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| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict` |
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| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
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| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
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| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name`|
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### Training settings
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??? note "Train"
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| Key | Value | Description |
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|------------------|--------|---------------------------------------------------------------------------------|
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| device | '' | cuda device, i.e. 0 or 0,1,2,3 or cpu. `''` selects available cuda 0 device |
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| epochs | 100 | Number of epochs to train |
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| workers | 8 | Number of cpu workers used per process. Scales automatically with DDP |
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| batch_size | 16 | Batch size of the dataloader |
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| imgsz | 640 | Image size of data in dataloader |
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| optimizer | SGD | Optimizer used. Supported optimizer are: `Adam`, `SGD`, `RMSProp` |
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| single_cls | False | Train on 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 | Enable rectangular training |
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| cos_lr | False | Use cosine LR scheduler |
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| lr0 | 0.01 | Initial learning rate |
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| lrf | 0.01 | Final OneCycleLR learning rate |
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| momentum | 0.937 | Use as `momentum` for SGD and `beta1` for Adam |
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| weight_decay | 0.0005 | Optimizer weight decay |
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| warmup_epochs | 3.0 | Warmup epochs. Fractions are 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 | 0.05 | Box loss gain |
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| cls | 0.5 | cls loss gain |
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| cls_pw | 1.0 | cls BCELoss positive_weight |
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| obj | 1.0 | bj loss gain (scale with pixels) |
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| obj_pw | 1.0 | obj BCELoss positive_weight |
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| iou_t | 0.20 | IOU training threshold |
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| anchor_t | 4.0 | anchor-multiple threshold |
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| fl_gamma | 0.0 | focal loss gamma |
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| label_smoothing | 0.0 | |
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| nbs | 64 | nominal batch size |
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| overlap_mask | `True` | **Segmentation**: Use mask overlapping during training |
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| mask_ratio | 4 | **Segmentation**: Set mask downsampling |
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| dropout | `False`| **Classification**: Use dropout while training |
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### Prediction Settings
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??? note "Prediction"
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| Key | Value | Description |
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|----------------|----------------------|----------------------------------------------------|
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| source | `ultralytics/assets` | Input source. Accepts image, folder, video, url |
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| view_img | `False` | View the prediction images |
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| save_txt | `False` | Save the results in a txt file |
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| save_conf | `False` | Save the condidence scores |
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| save_crop | `Fasle` | |
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| hide_labels | `False` | Hide the labels |
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| hide_conf | `False` | Hide the confidence scores |
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| vid_stride | `False` | Input 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` | Augmented inference |
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| agnostic_nms | `False` | Class-agnostic NMS |
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| retina_masks | `False` | **Segmentation:** High resolution masks |
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### Validation settings
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??? note "Validation"
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| Key | Value | Description |
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|-------------|---------|-----------------------------------|
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| noval | `False` | ??? |
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| save_json | `False` | |
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| save_hybrid | `False` | |
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| conf_thres | `0.001` | Confidence threshold |
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| iou_thres | `0.6` | IoU threshold |
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| max_det | `300` | Maximum number of detections |
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| half | `True` | Use .half() mode. |
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| dnn | `False` | Use OpenCV DNN for ONNX inference |
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| plots | `False` | |
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### Augmentation settings
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??? note "Augmentation"
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| hsv_h | 0.015 | Image HSV-Hue augmentation (fraction) |
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|-------------|-------|-------------------------------------------------|
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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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### Logging, checkpoints, plotting and file management
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??? note "files"
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| Key | Value | Description |
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|-----------|---------|---------------------------------------------------------------------------------------------|
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| project: | 'runs' | The project name |
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| name: | 'exp' | The run name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
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| exist_ok: | `False` | ??? |
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| plots | `False` | **Validation**: Save plots while validation |
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| nosave | `False` | Don't save any plots, models or files | |