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110 lines
5.3 KiB
110 lines
5.3 KiB
# YOLO 🚀 by Ultralytics, GPL-3.0 license |
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# Default training settings and hyperparameters for medium-augmentation COCO training |
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task: "detect" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run. |
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mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in. |
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# Train settings ------------------------------------------------------------------------------------------------------- |
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model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file |
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data: null # i.e. coco128.yaml. Path to data file |
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epochs: 100 # number of epochs to train for |
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patience: 50 # TODO: epochs to wait for no observable improvement for early stopping of training |
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batch: 16 # number of images per batch |
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imgsz: 640 # size of input images |
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save: True # save checkpoints |
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cache: False # True/ram, disk or False. Use cache for data loading |
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device: '' # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on |
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workers: 8 # number of worker threads for data loading |
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project: null # project name |
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name: null # 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: False # 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 |
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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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# Segmentation |
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overlap_mask: True # masks should overlap during training |
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mask_ratio: 4 # mask downsample ratio |
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# Classification |
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dropout: False # use dropout regularization |
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# Val/Test settings ---------------------------------------------------------------------------------------------------- |
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val: True # validate/test during training |
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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: null # 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 # show plots during training |
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# Prediction settings -------------------------------------------------------------------------------------------------- |
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source: null # 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 results |
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augment: False # apply data augmentation to images |
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agnostic_nms: False # class-agnostic NMS |
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retina_masks: False # use retina masks for object detection |
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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: 17 # ONNX: opset version |
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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 (SGD=1E-2, Adam=1E-3) |
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lrf: 0.01 # final OneCycleLR 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 |
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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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# Hydra configs -------------------------------------------------------------------------------------------------------- |
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hydra: |
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output_subdir: null # disable hydra directory creation |
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run: |
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dir: . |
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# Debug, do not modify ------------------------------------------------------------------------------------------------- |
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v5loader: False # use legacy YOLOv5 dataloader
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