You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
108 lines
5.3 KiB
108 lines
5.3 KiB
# Ultralytics YOLO 🚀, 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 # 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)
|
|
|
|
# Custom config.yaml ---------------------------------------------------------------------------------------------------
|
|
cfg: null # for overriding defaults.yaml
|
|
|
|
# Debug, do not modify -------------------------------------------------------------------------------------------------
|
|
v5loader: False # use legacy YOLOv5 dataloader
|