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115 lines
6.0 KiB
115 lines
6.0 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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# Default training settings and hyperparameters for medium-augmentation COCO training |
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task: detect # YOLO task, i.e. detect, segment, classify, pose |
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mode: train # YOLO mode, i.e. train, val, predict, export, track, benchmark |
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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. 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, results saved to 'project/name' directory |
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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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rect: False # rectangular training if mode='train' or rectangular validation if mode='val' |
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cos_lr: False # use cosine learning rate scheduler |
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close_mosaic: 0 # (int) disable mosaic augmentation for final epochs |
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resume: False # resume training from last checkpoint |
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amp: True # Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check |
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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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show_labels: True # show object labels in plots |
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show_conf: True # show object confidence scores in plots |
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vid_stride: 1 # video frame-rate stride |
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line_width: # line width of the bounding boxes |
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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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pose: 12.0 # pose loss gain |
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kobj: 1.0 # keypoint obj loss gain |
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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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