# Prediction settings -------------------------------------------------------------------------------------------------- source: # 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 model features augment: False # apply image augmentation to prediction sources agnostic_nms: False # class-agnostic NMS classes: # filter results by class, i.e. class=0, or class=[0,2,3] retina_masks: False # use high-resolution segmentation masks boxes: True # Show boxes in segmentation predictions解释

时间: 2023-08-15 20:47:48 浏览: 205

这些参数是用于目标检测任务的模型预测设置，具体含义如下： - source：目标检测任务的输入源，可以是图片或视频的目录。 - show：是否在屏幕上显示结果。 - save_txt：是否将结果保存为.txt文件。 - save_conf：是否将结果保存为包括置信度得分的结果。 - save_crop：是否保存裁剪后的图像及其结果。 - hide_labels：是否隐藏结果中的标签。 - hide_conf：是否隐藏结果中的置信度得分。 - vid_stride：在视频帧中按多少步长执行预测。 - line_thickness：边界框的线条粗细（像素）。 - visualize：是否可视化模型的特征。 - augment：是否对预测源应用图像增强。 - agnostic_nms：是否采用无类别NMS。 - classes：过滤特定类别的结果，例如class=0或class=[0,2,3]。 - retina_masks：是否使用高分辨率分割掩模。 - boxes：在分割预测中是否显示边界框。

### ------- input/output length settings -------------- parser.add_argument('--seq_len', type=int, default=96, help='input sequence length of LightTS, look back window') parser.add_argument('--pred_len', type=int, default=48, help='prediction sequence length, horizon') parser.add_argument('--lastWeight', type=float, default=1.0)

这段代码是在创建一些命令行参数，用于设置输入和输出序列的长度。具体来说，它使用了 `add_argument()` 方法来添加三个参数： 1. `--seq_len`：表示输入序列的长度，即LightTS的窗口大小，它是一个整数类型，可以被指定但不是必须的，默认为 `96`。 2. `--pred_len`：表示预测序列的长度，即预测的时间步数，它是一个整数类型，可以被指定但不是必须的，默认为 `48`。 3. `--lastWeight`：表示最后一个时间步的权重，它是一个浮点类型，可以被指定但不是必须的，默认为 `1.0`。这些参数的作用分别是： - `seq_len`：决定了模型每次接收多少个时间步的输入序列。 - `pred_len`：决定了模型需要预测多少个时间步的输出序列。 - `lastWeight`：在计算损失函数时，最后一个时间步的预测值会乘以这个权重，用于调整最后一个时间步的预测精度。用户可以根据自己的需求来指定这些参数的值。

# Ultralytics YOLO 🚀, AGPL-3.0 license # Default training settings and hyperparameters for medium-augmentation COCO training #中等增强的COCO训练的默认训练设置和超参数 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: 20 # (int) epochs to wait for no observable improvement for early stopping of training batch: 20 # (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: 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 # Segmentation overlap_mask: True # (bool) masks should overlap during training (segment train only) mask_ratio: 2 # (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: 0.3 # 置信度(float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val) iou: 0.8 # (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: True # (bool) use high-resolution segmentation masks # 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 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 # (float) image HSV-Hue augmentation (fraction) 0.015 hsv_s: 0 # (float) image HSV-Saturation augmentation (fraction)0.7 hsv_v: 0 # (float) image HSV-Value augmentation (fraction)0.4 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 # (float) image flip up-down (probability)0.5 fliplr: 0 # (float) image flip left-right (probability)0.5 mosaic: 1 # (float) image mosaic (probability)1.0 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 # Tracker settings ------------------------------------------------------------------------------------------------------ tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml] 帮我介绍一下这些参数

### Ultralytics YOLO 配置文件参数详解 Ultralytics YOLO 的配置文件（如 `yolov8s-seg.yaml`）用于定义模型的结构、训练参数和数据处理方式。理解这些参数的含义及其用途对于优化模型性能至关重要。 #### 模型结构参数配置文件中的模型结构参数定义了网络的骨干（backbone）和检测头（head）的结构。这些参数直接影响模型的特征提取能力和推理速度。 - **backbone**：定义了模型的骨干网络，通常使用CSPDarknet结构。骨干网络负责从输入图像中提取多尺度特征。 - **head**：定义了检测头的结构，通常包含PANet（Path Aggregation Network）模块。检测头负责生成边界框、类别预测和掩膜预测。 - **depth_multiple**：控制网络深度的倍数。例如，`depth_multiple=0.33` 表示将标准网络的深度缩小为原来的三分之一。 - **width_multiple**：控制网络宽度的倍数。例如，`width_multiple=0.5` 表示将标准网络的宽度缩小为原来的一半。 #### 数据处理参数数据处理参数定义了输入图像的预处理方式和数据增强策略。这些参数对模型的泛化能力和鲁棒性有重要影响。 - **imgsz**：输入图像的尺寸。例如，`imgsz=640` 表示输入图像的大小为640×640像素。 - **augment**：是否启用数据增强。数据增强可以提高模型的泛化能力。 - **hsv_h**、**hsv_s**、**hsv_v**：定义HSV颜色空间的随机增强范围。这些参数用于调整图像的色调、饱和度和亮度。 - **degrees**、**translate**、**scale**、**shear**：定义图像的旋转、平移、缩放和剪切增强范围。 - **flipud**、**fliplr**：定义图像的上下翻转和左右翻转概率。 #### 训练参数训练参数定义了模型的训练过程，包括优化器、学习率和损失函数。 - **optimizer**：指定优化器的类型，如SGD或Adam。 - **lr0**：初始学习率。 - **lrf**：学习率的最终值，通常用于学习率衰减。 - **momentum**：动量参数，用于优化器。 - **weight_decay**：权重衰减系数，用于防止过拟合。 - **warmup_epochs**：学习率预热的轮数。 - **cos_lr**：是否使用余弦学习率衰减策略。 - **loss**：指定损失函数的类型，如CIoU损失或DIoU损失。 #### 推理参数推理参数定义了模型在推理阶段的行为，包括置信度阈值和非极大值抑制（NMS）阈值。 - **conf**：置信度阈值，用于过滤低置信度的预测框。 - **iou**：NMS的IoU阈值，用于去除重叠的预测框。 #### 配置文件示例以下是一个典型的YOLOv8配置文件示例： ```yaml # YOLOv8 configuration for segmentation tasks # Model parameters backbone: name: CSPDarknet depth_multiple: 0.33 width_multiple: 0.5 out_indices: [2, 3, 4] head: name: SegmentationHead in_channels: [128, 256, 512] num_classes: 80 mask_resolution: 160 # Data parameters imgsz: 640 augment: True hsv_h: 0.015 hsv_s: 0.7 hsv_v: 0.4 degrees: 0.0 translate: 0.1 scale: 0.5 shear: 0.0 flipud: 0.5 fliplr: 0.5 # Training parameters optimizer: SGD lr0: 0.01 lrf: 0.01 momentum: 0.937 weight_decay: 0.0005 warmup_epochs: 3 cos_lr: True loss: CIoULoss # Inference parameters conf: 0.25 iou: 0.45 ``` #### 相关问题 1. 如何在YOLOv8中调整数据增强策略？ 2. YOLOv8的配置文件支持自定义模型结构吗？ 3. 如何在YOLOv8中设置不同的损失函数？

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