yolov10/docs/conf.md

Ultralytics YOLO

Default training settings and hyperparameters for medium-augmentation COCO training

Setting the operation type

???+ note "Operation"

| Key    | Value    | Description                                                                                                                                                                                 |
|--------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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 |
| mode  | `train`  | Set the mode via CLI. It can be `train`, `val`, `predict`   |
| resume  | `False`  | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed  |
| model | null     | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt`                                                                                            |
| data  | null     | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name`|

Training settings

??? note "Train" | Key | Value | Description | |------------------|--------|---------------------------------------------------------------------------------| | device | '' | cuda device, i.e. 0 or 0,1,2,3 or cpu. '' selects available cuda 0 device | | epochs | 100 | Number of epochs to train | | workers | 8 | Number of cpu workers used per process. Scales automatically with DDP | | batch_size | 16 | Batch size of the dataloader | | img_size | 640 | Image size of data in dataloader | | optimizer | SGD | Optimizer used. Supported optimizer are: Adam, SGD, RMSProp | | single_cls | False | Train on multi-class data as single-class | | image_weights | False | Use weighted image selection for training | | rect | False | Enable rectangular training | | cos_lr | False | Use cosine LR scheduler | | lr0 | 0.01 | Initial learning rate | | lrf | 0.01 | Final OneCycleLR learning rate | | momentum | 0.937 | Use as momentum for SGD and beta1 for Adam | | weight_decay | 0.0005 | Optimizer weight decay | | warmup_epochs | 3.0 | Warmup epochs. Fractions are ok. | | warmup_momentum | 0.8 | Warmup initial momentum | | warmup_bias_lr | 0.1 | Warmup initial bias lr | | box | 0.05 | Box loss gain | | cls | 0.5 | cls loss gain | | cls_pw | 1.0 | cls BCELoss positive_weight | | obj | 1.0 | bj loss gain (scale with pixels) | | obj_pw | 1.0 | obj BCELoss positive_weight | | iou_t | 0.20 | IOU training threshold | | anchor_t | 4.0 | anchor-multiple threshold | | fl_gamma | 0.0 | focal loss gamma | | label_smoothing | 0.0 | | | nbs | 64 | nominal batch size | | overlap_mask | True | Segmentation: Use mask overlapping during training | | mask_ratio | 4 | Segmentation: Set mask downsampling | | dropout | False| Classification: Use dropout while training |

Prediction Settings

??? note "Prediction" | Key | Value | Description | |----------------|----------------------|----------------------------------------------------| | source | ultralytics/assets | Input source. Accepts image, folder, video, url | | view_img | False | View the prediction images | | save_txt | False | Save the results in a txt file | | save_conf | False | Save the condidence scores | | save_crop | Fasle | | | hide_labels | False | Hide the labels | | hide_conf | False | Hide the confidence scores | | vid_stride | False | Input video frame-rate stride | | line_thickness | 3 | Bounding-box thickness (pixels) | | visualize | False | Visualize model features | | augment | False | Augmented inference | | agnostic_nms | False | Class-agnostic NMS | | retina_masks | False | Segmentation: High resolution masks |

Validation settings

??? note "Validation" | Key | Value | Description | |-------------|---------|-----------------------------------| | noval | False | ??? | | save_json | False | | | save_hybrid | False | | | conf_thres | 0.001 | Confidence threshold | | iou_thres | 0.6 | IoU threshold | | max_det | 300 | Maximum number of detections | | half | True | Use .half() mode. | | dnn | False | Use OpenCV DNN for ONNX inference | | plots | False | |

Augmentation settings

??? note "Augmentation"

| 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

??? note "files" | Key | Value | Description | |-----------|---------|---------------------------------------------------------------------------------------------| | project: | 'runs' | The project name | | name: | 'exp' | The run name. exp gets automatically incremented if not specified, i.e, exp, exp2 ... | | exist_ok: | False | ??? | | plots | False | Validation: Save plots while validation | | nosave | False | Don't save any plots, models or files |