修改配置文件：使用cpu

ultralytics/cfg/default.yaml

@@ -1,114 +1,114 @@
-# 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
-
-# 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
-patience: 50  # (int) epochs to wait for no observable improvement for early stopping of training
-batch: -1  # (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: 0 # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
-workers: 2 # (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: True  # (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
-resume: False  # (bool) resume training from last checkpoint
-amp: False  # (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
-# 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: True  # (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 during train/val
-
-# Prediction settings --------------------------------------------------------------------------------------------------
-source:  # (str, optional) source directory for images or videos
-show: False  # (bool) show results if possible
-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 object labels in plots
-show_conf: True  # (bool) show object confidence scores in plots
-vid_stride: 1  # (int) video frame-rate stride
-line_width:   # (int, optional) line width of the bounding boxes, auto if missing
-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. class=0, or class=[0,2,3]
-retina_masks: False  # (bool) use high-resolution segmentation masks
-boxes: True  # (bool) Show boxes in segmentation predictions
-
-# 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
-
-# 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)
-
-# Custom config.yaml ---------------------------------------------------------------------------------------------------
-cfg:  # (str, optional) for overriding defaults.yaml
-save_dir: ./runs/train1 # 自己设置路径
-# Tracker settings ------------------------------------------------------------------------------------------------------
-tracker: botsort.yaml  # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
+# 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
+
+# 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
+patience: 50  # (int) epochs to wait for no observable improvement for early stopping of training
+batch: -1  # (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: cpu # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
+workers: 2 # (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: True  # (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
+resume: False  # (bool) resume training from last checkpoint
+amp: False  # (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
+# 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: True  # (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 during train/val
+
+# Prediction settings --------------------------------------------------------------------------------------------------
+source:  # (str, optional) source directory for images or videos
+show: False  # (bool) show results if possible
+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 object labels in plots
+show_conf: True  # (bool) show object confidence scores in plots
+vid_stride: 1  # (int) video frame-rate stride
+line_width:   # (int, optional) line width of the bounding boxes, auto if missing
+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. class=0, or class=[0,2,3]
+retina_masks: False  # (bool) use high-resolution segmentation masks
+boxes: True  # (bool) Show boxes in segmentation predictions
+
+# 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
+
+# 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)
+
+# Custom config.yaml ---------------------------------------------------------------------------------------------------
+cfg:  # (str, optional) for overriding defaults.yaml
+save_dir: ./runs/train1 # 自己设置路径
+# Tracker settings ------------------------------------------------------------------------------------------------------
+tracker: botsort.yaml  # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]