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90 lines
3.2 KiB
90 lines
3.2 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 and Mode
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task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
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mode: "train" # choice=['train', 'val', 'infer']
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# Train settings -------------------------------------------------------------------------------------------------------
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model: null # i.e. yolov5s.pt, yolo.yaml
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data: null # i.e. coco128.yaml
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epochs: 300
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batch_size: 16
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img_size: 640
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nosave: False
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cache: False # True/ram, disk or False
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device: '' # cuda device, i.e. 0 or 0,1,2,3 or cpu
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workers: 8
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project: 'runs'
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name: 'exp'
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exist_ok: False
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pretrained: False
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optimizer: 'SGD' # choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
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verbose: False
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seed: 0
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deterministic: True
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local_rank: -1
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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 LR scheduler
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# Segmentation
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overlap_mask: True # masks overlap
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mask_ratio: 4 # mask downsample ratio
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# Classification
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dropout: False # use dropout
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# Val/Test settings ----------------------------------------------------------------------------------------------------
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noval: False
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save_json: False
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save_hybrid: False
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conf_thres: 0.001
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iou_thres: 0.6
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max_det: 300
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half: True
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dnn: False # use OpenCV DNN for ONNX inference
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plots: False
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save_txt: False
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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: 0.05 # box loss gain
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cls: 0.5 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 1.0 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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# anchors: 3 # anchors per output layer (0 to ignore)
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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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# anchors: 3
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augment_hyp:
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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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