| # Ultralytics YOLO π, 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 | |
| # 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: 100 # (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) | |
| val_period: 1 # (int) Validation every x epochs | |
| 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 multiscale 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) | |
| # Classification | |
| 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 | |
| # 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 | |
| # 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. | |
| # 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 using `onnxslim` | |
| 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) | |
| bgr: 0.0 # (float) image channel BGR (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 | |
| # Tracker settings ------------------------------------------------------------------------------------------------------ | |
| tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml] | |