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116 lines
6.0 KiB
116 lines
6.0 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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# Default training settings and hyperparameters for medium-augmentation COCO training
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task: detect # inference task, i.e. detect, segment, classify
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mode: train # YOLO mode, i.e. train, val, predict, export
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# Train settings -------------------------------------------------------------------------------------------------------
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model: # path to model file, i.e. yolov8n.pt, yolov8n.yaml
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data: # path to data file, i.e. 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: # 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: # project name
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name: # 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: True # 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 if mode='train', support rectangular evaluation if mode='val'
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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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min_memory: False # minimize memory footprint loss function, choices=[False, True, <roll_out_thr>]
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# Segmentation
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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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# Classification
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dropout: 0.0 # use dropout regularization (classify train only)
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# Val/Test settings ----------------------------------------------------------------------------------------------------
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val: True # validate/test during training
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split: val # dataset split to use for validation, i.e. 'val', 'test' or 'train'
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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: # object confidence threshold for detection (default 0.25 predict, 0.001 val)
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iou: 0.7 # 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: False # use half precision (FP16)
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dnn: False # use OpenCV DNN for ONNX inference
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plots: True # save plots during train/val
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# Prediction settings --------------------------------------------------------------------------------------------------
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source: # source directory for images or videos
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show: False # show results if possible
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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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vid_stride: 1 # 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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classes: # filter results by class, i.e. class=0, or class=[0,2,3]
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retina_masks: False # use high-resolution segmentation masks
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boxes: True # Show boxes in segmentation predictions
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# Export settings ------------------------------------------------------------------------------------------------------
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format: torchscript # format to export to
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keras: False # use Keras
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optimize: False # TorchScript: optimize for mobile
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int8: False # CoreML/TF 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: # ONNX: opset version (optional)
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workspace: 4 # TensorRT: workspace size (GB)
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nms: False # CoreML: add NMS
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# Hyperparameters ------------------------------------------------------------------------------------------------------
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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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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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# Custom config.yaml ---------------------------------------------------------------------------------------------------
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cfg: # for overriding defaults.yaml
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# Debug, do not modify -------------------------------------------------------------------------------------------------
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v5loader: False # use legacy YOLOv5 dataloader
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# Tracker settings ------------------------------------------------------------------------------------------------------
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tracker: botsort.yaml # tracker type, ['botsort.yaml', 'bytetrack.yaml']
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