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# Ultralytics YOLO π, GPL-3.0 license | |
# Default training settings and hyperparameters for medium-augmentation COCO training | |
task: "detect" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run. | |
mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in. | |
# Train settings ------------------------------------------------------------------------------------------------------- | |
model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file | |
data: null # i.e. coco128.yaml. Path to data file | |
epochs: 100 # number of epochs to train for | |
patience: 50 # TODO: epochs to wait for no observable improvement for early stopping of training | |
batch: 16 # number of images per batch | |
imgsz: 640 # size of input images | |
save: True # save checkpoints | |
cache: False # True/ram, disk or False. Use cache for data loading | |
device: null # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on | |
workers: 8 # number of worker threads for data loading | |
project: null # project name | |
name: null # experiment name | |
exist_ok: False # whether to overwrite existing experiment | |
pretrained: False # whether to use a pretrained model | |
optimizer: 'SGD' # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] | |
verbose: False # whether to print verbose output | |
seed: 0 # random seed for reproducibility | |
deterministic: True # whether to enable deterministic mode | |
single_cls: False # train multi-class data as single-class | |
image_weights: False # use weighted image selection for training | |
rect: False # support rectangular training | |
cos_lr: False # use cosine learning rate scheduler | |
close_mosaic: 10 # disable mosaic augmentation for final 10 epochs | |
resume: False # resume training from last checkpoint | |
# Segmentation | |
overlap_mask: True # masks should overlap during training | |
mask_ratio: 4 # mask downsample ratio | |
# Classification | |
dropout: 0.0 # use dropout regularization | |
# Val/Test settings ---------------------------------------------------------------------------------------------------- | |
val: True # validate/test during training | |
save_json: False # save results to JSON file | |
save_hybrid: False # save hybrid version of labels (labels + additional predictions) | |
conf: null # object confidence threshold for detection (default 0.25 predict, 0.001 val) | |
iou: 0.7 # intersection over union (IoU) threshold for NMS | |
max_det: 300 # maximum number of detections per image | |
half: False # use half precision (FP16) | |
dnn: False # use OpenCV DNN for ONNX inference | |
plots: True # show plots during training | |
# Prediction settings -------------------------------------------------------------------------------------------------- | |
source: null # source directory for images or videos | |
show: False # show results if possible | |
save_txt: False # save results as .txt file | |
save_conf: False # save results with confidence scores | |
save_crop: False # save cropped images with results | |
hide_labels: False # hide labels | |
hide_conf: False # hide confidence scores | |
vid_stride: 1 # video frame-rate stride | |
line_thickness: 3 # bounding box thickness (pixels) | |
visualize: False # visualize results | |
augment: False # apply data augmentation to images | |
agnostic_nms: False # class-agnostic NMS | |
retina_masks: False # use retina masks for object detection | |
# Export settings ------------------------------------------------------------------------------------------------------ | |
format: torchscript # format to export to | |
keras: False # use Keras | |
optimize: False # TorchScript: optimize for mobile | |
int8: False # CoreML/TF INT8 quantization | |
dynamic: False # ONNX/TF/TensorRT: dynamic axes | |
simplify: False # ONNX: simplify model | |
opset: 17 # ONNX: opset version | |
workspace: 4 # TensorRT: workspace size (GB) | |
nms: False # CoreML: add NMS | |
# Hyperparameters ------------------------------------------------------------------------------------------------------ | |
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) | |
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) | |
momentum: 0.937 # SGD momentum/Adam beta1 | |
weight_decay: 0.0005 # optimizer weight decay 5e-4 | |
warmup_epochs: 3.0 # warmup epochs (fractions ok) | |
warmup_momentum: 0.8 # warmup initial momentum | |
warmup_bias_lr: 0.1 # warmup initial bias lr | |
box: 7.5 # box loss gain | |
cls: 0.5 # cls loss gain (scale with pixels) | |
dfl: 1.5 # dfl loss gain | |
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) | |
label_smoothing: 0.0 | |
nbs: 64 # nominal batch size | |
hsv_h: 0.015 # image HSV-Hue augmentation (fraction) | |
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) | |
hsv_v: 0.4 # image HSV-Value augmentation (fraction) | |
degrees: 0.0 # image rotation (+/- deg) | |
translate: 0.1 # image translation (+/- fraction) | |
scale: 0.5 # image scale (+/- gain) | |
shear: 0.0 # image shear (+/- deg) | |
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 | |
flipud: 0.0 # image flip up-down (probability) | |
fliplr: 0.5 # image flip left-right (probability) | |
mosaic: 1.0 # image mosaic (probability) | |
mixup: 0.0 # image mixup (probability) | |
copy_paste: 0.0 # segment copy-paste (probability) | |
# Hydra configs -------------------------------------------------------------------------------------------------------- | |
hydra: | |
output_subdir: null # disable hydra directory creation | |
run: | |
dir: . | |
# Debug, do not modify ------------------------------------------------------------------------------------------------- | |
v5loader: False # use legacy YOLOv5 dataloader | |