# 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: # 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: # 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, ] # 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 split: val # dataset split to use for validation, i.e. 'val', 'test' or 'train' 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: False # show results if possible 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 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 # Tracker settings ------------------------------------------------------------------------------------------------------ tracker: botsort.yaml # tracker type, ['botsort.yaml', 'bytetrack.yaml']