| # Ultralytics YOLO 🚀, GPL-3.0 license | |
| # Default training settings and hyperparameters for medium-augmentation COCO training | |
| task: detect # inference task, i.e. detect, segment, classify | |
| mode: train # YOLO mode, i.e. train, val, predict, export | |
| # Train settings ------------------------------------------------------------------------------------------------------- | |
| model: # path to model file, i.e. yolov8n.pt, yolov8n.yaml | |
| data: "./coco.yaml" # path to data file, i.e. 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: 1 # 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 | |
| cache: False # True/ram, disk or False. Use cache for data loading | |
| device: # 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: # project name | |
| name: # 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: True # 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 if mode='train', support rectangular evaluation if mode='val' | |
| 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 | |
| min_memory: False # minimize memory footprint loss function, choices=[False, True, <roll_out_thr>] | |
| sync_bn: False # convert batchnorm to syncbatchnorm in model | |
| nndct_quant: False # True for quant model | |
| quant_mode: 'test' # calib or test | |
| dump_xmodel: False # True for dump xmodel | |
| dump_onnx: False # True for dump onnx | |
| onnx_weight: "./yolov8m_qat.onnx" | |
| onnx_runtime: False | |
| ipu: False | |
| provider_config: '' | |
| # Segmentation | |
| overlap_mask: True # masks should overlap during training (segment train only) | |
| mask_ratio: 4 # mask downsample ratio (segment train only) | |
| # Classification | |
| dropout: 0.0 # use dropout regularization (classify train only) | |
| # Val/Test settings ---------------------------------------------------------------------------------------------------- | |
| val: True # validate/test during training | |
| save_json: False # save results to JSON file | |
| save_hybrid: False # save hybrid version of labels (labels + additional predictions) | |
| conf: # object confidence threshold for detection (default 0.25 predict, 0.001 val) | |
| iou: 0.7 # intersection over union (IoU) threshold for NMS | |
| max_det: 300 # maximum number of detections per image | |
| half: False # use half precision (FP16) | |
| dnn: False # use OpenCV DNN for ONNX inference | |
| plots: True # save plots during train/val | |
| # Prediction settings -------------------------------------------------------------------------------------------------- | |
| source: # source directory for images or videos | |
| show: True # show results if possible | |
| save_txt: True # 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 | |
| vid_stride: 1 # 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 | |
| classes: # filter results by class, i.e. class=0, or class=[0,2,3] | |
| retina_masks: False # use high-resolution segmentation masks | |
| boxes: True # Show boxes in segmentation predictions | |
| # Export settings ------------------------------------------------------------------------------------------------------ | |
| format: torchscript # format to export to | |
| keras: False # use Keras | |
| optimize: False # TorchScript: optimize for mobile | |
| int8: False # CoreML/TF INT8 quantization | |
| dynamic: False # ONNX/TF/TensorRT: dynamic axes | |
| simplify: False # ONNX: simplify model | |
| opset: # ONNX: opset version (optional) | |
| workspace: 4 # TensorRT: workspace size (GB) | |
| nms: False # CoreML: add NMS | |
| # Hyperparameters ------------------------------------------------------------------------------------------------------ | |
| 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 | |
| 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) | |
| # Custom config.yaml --------------------------------------------------------------------------------------------------- | |
| cfg: # for overriding defaults.yaml | |
| # Debug, do not modify ------------------------------------------------------------------------------------------------- | |
| v5loader: False # use legacy YOLOv5 dataloader | |