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111 lines
5.3 KiB
111 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: 0.001 # object confidence threshold for detection
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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: "ultralytics/assets" # 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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