Delete ultralytics/default.yaml

ultralytics/default.yaml

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-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Global configuration YAML with settings and hyperparameters for YOLO training, validation, prediction and export
-# For documentation see https://docs.ultralytics.com/usage/cfg/
-
-task: detect # (str) YOLO task, i.e. detect, segment, classify, pose, obb
-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. coco8.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[h,w] 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 multiscale during training
-# Segmentation
-overlap_mask: True # (bool) merge object masks into a single image mask 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: True # (bool) ONNX: simplify model using `onnxslim`
-opset: # (int, optional) ONNX: opset version
-workspace: None # (float, optional) TensorRT: workspace size (GiB), `None` will let TensorRT auto-allocate memory
-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: 0.0 # 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
-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: 0.1 # (float) segment copy-paste (probability)
-
-copy_paste_mode: "flip" # (str) the method to do copy_paste augmentation (flip, mixup)
-auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
-erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
-crop_fraction: 1.0 # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.
-
-# Custom config.yaml ---------------------------------------------------------------------------------------------------
-cfg: # (str, optional) for overriding defaults.yaml
-
-# Tracker settings ------------------------------------------------------------------------------------------------------
-tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]