Glenn Jocher
10 months ago
committed by
GitHub
parent
d021524e85
commit
63e7db1dac
55 changed files with 725 additions and 756 deletions
@ -1,125 +1,125 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
# Default training settings and hyperparameters for medium-augmentation COCO training |
||||
|
||||
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose |
||||
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark |
||||
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose |
||||
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark |
||||
|
||||
# Train settings ------------------------------------------------------------------------------------------------------- |
||||
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml |
||||
data: # (str, optional) path to data file, i.e. coco128.yaml |
||||
epochs: 100 # (int) number of epochs to train for |
||||
time: # (float, optional) number of hours to train for, overrides epochs if supplied |
||||
patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training |
||||
batch: 16 # (int) number of images per batch (-1 for AutoBatch) |
||||
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes |
||||
save: True # (bool) save train checkpoints and predict results |
||||
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml |
||||
data: # (str, optional) path to data file, i.e. coco128.yaml |
||||
epochs: 100 # (int) number of epochs to train for |
||||
time: # (float, optional) number of hours to train for, overrides epochs if supplied |
||||
patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training |
||||
batch: 16 # (int) number of images per batch (-1 for AutoBatch) |
||||
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes |
||||
save: True # (bool) save train checkpoints and predict results |
||||
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1) |
||||
cache: False # (bool) True/ram, disk or False. Use cache for data loading |
||||
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
||||
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP) |
||||
project: # (str, optional) project name |
||||
name: # (str, optional) experiment name, results saved to 'project/name' directory |
||||
exist_ok: False # (bool) whether to overwrite existing experiment |
||||
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str) |
||||
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] |
||||
verbose: True # (bool) whether to print verbose output |
||||
seed: 0 # (int) random seed for reproducibility |
||||
deterministic: True # (bool) whether to enable deterministic mode |
||||
single_cls: False # (bool) train multi-class data as single-class |
||||
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val' |
||||
cos_lr: False # (bool) use cosine learning rate scheduler |
||||
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable) |
||||
resume: False # (bool) resume training from last checkpoint |
||||
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check |
||||
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set) |
||||
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers |
||||
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training |
||||
multi_scale: False # (bool) Whether to use multi-scale during training |
||||
cache: False # (bool) True/ram, disk or False. Use cache for data loading |
||||
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
||||
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP) |
||||
project: # (str, optional) project name |
||||
name: # (str, optional) experiment name, results saved to 'project/name' directory |
||||
exist_ok: False # (bool) whether to overwrite existing experiment |
||||
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str) |
||||
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] |
||||
verbose: True # (bool) whether to print verbose output |
||||
seed: 0 # (int) random seed for reproducibility |
||||
deterministic: True # (bool) whether to enable deterministic mode |
||||
single_cls: False # (bool) train multi-class data as single-class |
||||
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val' |
||||
cos_lr: False # (bool) use cosine learning rate scheduler |
||||
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable) |
||||
resume: False # (bool) resume training from last checkpoint |
||||
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check |
||||
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set) |
||||
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers |
||||
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training |
||||
multi_scale: False # (bool) Whether to use multi-scale during training |
||||
# Segmentation |
||||
overlap_mask: True # (bool) masks should overlap during training (segment train only) |
||||
mask_ratio: 4 # (int) mask downsample ratio (segment train only) |
||||
overlap_mask: True # (bool) masks should overlap during training (segment train only) |
||||
mask_ratio: 4 # (int) mask downsample ratio (segment train only) |
||||
# Classification |
||||
dropout: 0.0 # (float) use dropout regularization (classify train only) |
||||
dropout: 0.0 # (float) use dropout regularization (classify train only) |
||||
|
||||
# Val/Test settings ---------------------------------------------------------------------------------------------------- |
||||
val: True # (bool) validate/test during training |
||||
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train' |
||||
save_json: False # (bool) save results to JSON file |
||||
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions) |
||||
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val) |
||||
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS |
||||
max_det: 300 # (int) maximum number of detections per image |
||||
half: False # (bool) use half precision (FP16) |
||||
dnn: False # (bool) use OpenCV DNN for ONNX inference |
||||
plots: True # (bool) save plots and images during train/val |
||||
val: True # (bool) validate/test during training |
||||
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train' |
||||
save_json: False # (bool) save results to JSON file |
||||
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions) |
||||
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val) |
||||
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS |
||||
max_det: 300 # (int) maximum number of detections per image |
||||
half: False # (bool) use half precision (FP16) |
||||
dnn: False # (bool) use OpenCV DNN for ONNX inference |
||||
plots: True # (bool) save plots and images during train/val |
||||
|
||||
# Predict settings ----------------------------------------------------------------------------------------------------- |
||||
source: # (str, optional) source directory for images or videos |
||||
vid_stride: 1 # (int) video frame-rate stride |
||||
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False) |
||||
visualize: False # (bool) visualize model features |
||||
augment: False # (bool) apply image augmentation to prediction sources |
||||
agnostic_nms: False # (bool) class-agnostic NMS |
||||
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3] |
||||
retina_masks: False # (bool) use high-resolution segmentation masks |
||||
embed: # (list[int], optional) return feature vectors/embeddings from given layers |
||||
source: # (str, optional) source directory for images or videos |
||||
vid_stride: 1 # (int) video frame-rate stride |
||||
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False) |
||||
visualize: False # (bool) visualize model features |
||||
augment: False # (bool) apply image augmentation to prediction sources |
||||
agnostic_nms: False # (bool) class-agnostic NMS |
||||
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3] |
||||
retina_masks: False # (bool) use high-resolution segmentation masks |
||||
embed: # (list[int], optional) return feature vectors/embeddings from given layers |
||||
|
||||
# Visualize settings --------------------------------------------------------------------------------------------------- |
||||
show: False # (bool) show predicted images and videos if environment allows |
||||
save_frames: False # (bool) save predicted individual video frames |
||||
save_txt: False # (bool) save results as .txt file |
||||
save_conf: False # (bool) save results with confidence scores |
||||
save_crop: False # (bool) save cropped images with results |
||||
show_labels: True # (bool) show prediction labels, i.e. 'person' |
||||
show_conf: True # (bool) show prediction confidence, i.e. '0.99' |
||||
show_boxes: True # (bool) show prediction boxes |
||||
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None. |
||||
show: False # (bool) show predicted images and videos if environment allows |
||||
save_frames: False # (bool) save predicted individual video frames |
||||
save_txt: False # (bool) save results as .txt file |
||||
save_conf: False # (bool) save results with confidence scores |
||||
save_crop: False # (bool) save cropped images with results |
||||
show_labels: True # (bool) show prediction labels, i.e. 'person' |
||||
show_conf: True # (bool) show prediction confidence, i.e. '0.99' |
||||
show_boxes: True # (bool) show prediction boxes |
||||
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None. |
||||
|
||||
# Export settings ------------------------------------------------------------------------------------------------------ |
||||
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats |
||||
keras: False # (bool) use Kera=s |
||||
optimize: False # (bool) TorchScript: optimize for mobile |
||||
int8: False # (bool) CoreML/TF INT8 quantization |
||||
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes |
||||
simplify: False # (bool) ONNX: simplify model |
||||
opset: # (int, optional) ONNX: opset version |
||||
workspace: 4 # (int) TensorRT: workspace size (GB) |
||||
nms: False # (bool) CoreML: add NMS |
||||
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats |
||||
keras: False # (bool) use Kera=s |
||||
optimize: False # (bool) TorchScript: optimize for mobile |
||||
int8: False # (bool) CoreML/TF INT8 quantization |
||||
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes |
||||
simplify: False # (bool) ONNX: simplify model |
||||
opset: # (int, optional) ONNX: opset version |
||||
workspace: 4 # (int) TensorRT: workspace size (GB) |
||||
nms: False # (bool) CoreML: add NMS |
||||
|
||||
# Hyperparameters ------------------------------------------------------------------------------------------------------ |
||||
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
||||
lrf: 0.01 # (float) final learning rate (lr0 * lrf) |
||||
momentum: 0.937 # (float) SGD momentum/Adam beta1 |
||||
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4 |
||||
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok) |
||||
warmup_momentum: 0.8 # (float) warmup initial momentum |
||||
warmup_bias_lr: 0.1 # (float) warmup initial bias lr |
||||
box: 7.5 # (float) box loss gain |
||||
cls: 0.5 # (float) cls loss gain (scale with pixels) |
||||
dfl: 1.5 # (float) dfl loss gain |
||||
pose: 12.0 # (float) pose loss gain |
||||
kobj: 1.0 # (float) keypoint obj loss gain |
||||
label_smoothing: 0.0 # (float) label smoothing (fraction) |
||||
nbs: 64 # (int) nominal batch size |
||||
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction) |
||||
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction) |
||||
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction) |
||||
degrees: 0.0 # (float) image rotation (+/- deg) |
||||
translate: 0.1 # (float) image translation (+/- fraction) |
||||
scale: 0.5 # (float) image scale (+/- gain) |
||||
shear: 0.0 # (float) image shear (+/- deg) |
||||
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001 |
||||
flipud: 0.0 # (float) image flip up-down (probability) |
||||
fliplr: 0.5 # (float) image flip left-right (probability) |
||||
mosaic: 1.0 # (float) image mosaic (probability) |
||||
mixup: 0.0 # (float) image mixup (probability) |
||||
copy_paste: 0.0 # (float) segment copy-paste (probability) |
||||
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix) |
||||
erasing: 0.4 # (float) probability of random erasing during classification training (0-1) |
||||
crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1) |
||||
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
||||
lrf: 0.01 # (float) final learning rate (lr0 * lrf) |
||||
momentum: 0.937 # (float) SGD momentum/Adam beta1 |
||||
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4 |
||||
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok) |
||||
warmup_momentum: 0.8 # (float) warmup initial momentum |
||||
warmup_bias_lr: 0.1 # (float) warmup initial bias lr |
||||
box: 7.5 # (float) box loss gain |
||||
cls: 0.5 # (float) cls loss gain (scale with pixels) |
||||
dfl: 1.5 # (float) dfl loss gain |
||||
pose: 12.0 # (float) pose loss gain |
||||
kobj: 1.0 # (float) keypoint obj loss gain |
||||
label_smoothing: 0.0 # (float) label smoothing (fraction) |
||||
nbs: 64 # (int) nominal batch size |
||||
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction) |
||||
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction) |
||||
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction) |
||||
degrees: 0.0 # (float) image rotation (+/- deg) |
||||
translate: 0.1 # (float) image translation (+/- fraction) |
||||
scale: 0.5 # (float) image scale (+/- gain) |
||||
shear: 0.0 # (float) image shear (+/- deg) |
||||
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001 |
||||
flipud: 0.0 # (float) image flip up-down (probability) |
||||
fliplr: 0.5 # (float) image flip left-right (probability) |
||||
mosaic: 1.0 # (float) image mosaic (probability) |
||||
mixup: 0.0 # (float) image mixup (probability) |
||||
copy_paste: 0.0 # (float) segment copy-paste (probability) |
||||
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix) |
||||
erasing: 0.4 # (float) probability of random erasing during classification training (0-1) |
||||
crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1) |
||||
|
||||
# Custom config.yaml --------------------------------------------------------------------------------------------------- |
||||
cfg: # (str, optional) for overriding defaults.yaml |
||||
cfg: # (str, optional) for overriding defaults.yaml |
||||
|
||||
# Tracker settings ------------------------------------------------------------------------------------------------------ |
||||
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml] |
||||
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml] |
||||
|
||||
@ -1,11 +1,11 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
# Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack |
||||
|
||||
tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack'] |
||||
track_high_thresh: 0.5 # threshold for the first association |
||||
track_low_thresh: 0.1 # threshold for the second association |
||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks |
||||
track_buffer: 30 # buffer to calculate the time when to remove tracks |
||||
match_thresh: 0.8 # threshold for matching tracks |
||||
tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack'] |
||||
track_high_thresh: 0.5 # threshold for the first association |
||||
track_low_thresh: 0.1 # threshold for the second association |
||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks |
||||
track_buffer: 30 # buffer to calculate the time when to remove tracks |
||||
match_thresh: 0.8 # threshold for matching tracks |
||||
# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now) |
||||
# mot20: False # for tracker evaluation(not used for now) |
||||
|
||||
Loading…
Reference in new issue