YOLOV8代码阅读
YOLO 训练参数
官网地址：训练参数
| Argument | Default | Description |
|---|---|---|
| model | None | Specifies the model file for training. Accepts a path to either a .pt pretrained model or a .yaml configuration file. Essential for defining the model structure or initializing weights. |
| data | None | Path to the dataset configuration file (e.g., coco8.yaml). This file contains dataset-specific parameters, including paths to training and validation data, class names, and number of classes. |
| epochs | 100 | Total number of training epochs. Each epoch represents a full pass over the entire dataset. Adjusting this value can affect training duration and model performance. |
| time | None | Maximum training time in hours. If set, this overrides the epochs argument, allowing training to automatically stop after the specified duration. Useful for time-constrained training scenarios. |
| patience | 100 | Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent overfitting by stopping training when performance plateaus. |
| batch | 16 | Batch size, with three modes: set as an integer (e.g., batch=16), auto mode for 60% GPU memory utilization (batch=-1), or auto mode with specified utilization fraction (batch=0.70). |
| imgsz | 640 | Target image size for training. All images are resized to this dimension before being fed into the model. Affects model accuracy and computational complexity. |
| save | True | Enables saving of training checkpoints and final model weights. Useful for resuming training or model deployment. |
| save_period | -1 | Frequency of saving model checkpoints, specified in epochs. A value of -1 disables this feature. Useful for saving interim models during long training sessions. |
| cache | False | Enables caching of dataset images in memory (True/ram), on disk (disk), or disables it (False). Improves training speed by reducing disk I/O at the cost of increased memory usage. |
| device | None | Specifies the computational device(s) for training: a single GPU (device=0), multiple GPUs (device=0,1), CPU (device=cpu), or MPS for Apple silicon (device=mps). |
| workers | 8 | Number of worker threads for data loading (per RANK if Multi-GPU training). Influences the speed of data preprocessing and feeding into the model, especially useful in multi-GPU setups. |
| project | None | Name of the project directory where training outputs are saved. Allows for organized storage of different experiments. |
| name | None | Name of the training run. Used for creating a subdirectory within the project folder, where training logs and outputs are stored. |
| exist_ok | False | If True, allows overwriting of an existing project/name directory. Useful for iterative experimentation without needing to manually clear previous outputs. |
| pretrained | True | Determines whether to start training from a pretrained model. Can be a boolean value or a string path to a specific model from which to load weights. Enhances training efficiency and model performance. |
| optimizer | ‘auto’ | Choice of optimizer for training. Options include SGD, Adam, AdamW, NAdam, RAdam, RMSProp etc., or auto for automatic selection based on model configuration. Affects convergence speed and stability. |
| seed | 0 | Sets the random seed for training, ensuring reproducibility of results across runs with the same configurations. |
| deterministic | True | Forces deterministic algorithm use, ensuring reproducibility but may affect performance and speed due to the restriction on non-deterministic algorithms. |
| single_cls | False | Treats all classes in multi-class datasets as a single class during training. Useful for binary classification tasks or when focusing on object presence rather than classification. |
| rect | False | Enables rectangular training, optimizing batch composition for minimal padding. Can improve efficiency and speed but may affect model accuracy. |
| cos_lr | False | Utilizes a cosine learning rate scheduler, adjusting the learning rate following a cosine curve over epochs. Helps in managing learning rate for better convergence. |
| close_mosaic | 10 | Disables mosaic data augmentation in the last N epochs to stabilize training before completion. Setting to 0 disables this feature. |
| resume | False | Resumes training from the last saved checkpoint. Automatically loads model weights, optimizer state, and epoch count, continuing training seamlessly. |
| amp | True | Enables Automatic Mixed Precision (AMP) training, reducing memory usage and possibly speeding up training with minimal impact on accuracy. |
| fraction | 1.0 | 指定用于训练的数据集的比例。允许在完整数据集的子集上进行训练，在资源有限或进行实验时非常有用。 |
| profile | False | 启用ONNX和TensorRT在训练期间的速率分析，有助于优化模型部署。 |
| freeze | None | 冻结模型的前N层或通过索引指定的层，减少可训练参数的数量。对于微调或迁移学习非常有用。 |
| lr0 | 0.01 | 初始学习率（例如，SGD=1E-2，Adam=1E-3）。调整这个值对于优化过程至关重要，影响模型权重的更新速度。 |
| lrf | 0.01 | 最终学习率作为初始率的分数 =（lr0 * lrf），与调度器结合使用，随着时间的推移调整学习率。 |
| momentum | 0.937 | SGD或Adam优化器的动量因子，影响当前更新中过去梯度的融合。 |
| weight_decay | 0.0005 | L2正则化项，惩罚大权重以防止过拟合。 |
| warmup_epochs | 3.0 | 学习率热身期的周期数，从低值逐渐增加到初始学习率，以在训练初期稳定训练。 |
| warmup_momentum | 0.8 | 热身阶段的初始动量，逐渐调整到设定的动量。 |
| warmup_bias_lr | 0.1 | 热身阶段偏置参数的学习率，帮助在初始周期稳定模型训练。 |
| box | 7.5 | 损失函数中框损失组件的权重，影响准确预测边界框坐标的重视程度。 |
| cls | 0.5 | 总损失函数中分类损失的权重，影响正确类别预测相对于其他组件的重要性。 |
| dfl | 1.5 | 分布焦点损失的权重，用于某些YOLO版本进行细粒度分类。 |
| pose | 12.0 | 在用于姿态估计的模型中姿态损失的权重，影响准确预测姿态关键点的重视程度。 |
| kobj | 2.0 | 姿态估计模型中关键点目标性损失的权重，平衡检测置信度与姿态准确性。 |
| label_smoothing | 0.0 | 应用标签平滑，将硬标签软化为目标标签和均匀分布标签的混合，可以提高泛化能力。 |
| nbs | 64 | 用于损失标准化的名义批量大小。 |
| overlap_mask | True | 确定是否应该将对象掩码合并为单个掩码进行训练，或者为每个对象保持单独的掩码。在重叠的情况下，较小的掩码在合并期间覆盖在较大的掩码之上。 |
| mask_ratio | 4 | 分段掩码的下采样比率，影响训练期间使用的掩码的分辨率。 |
| dropout | 0.0 | 分类任务中的正则化丢弃率，通过在训练期间随机省略单元来防止过拟合。 |
| val | True | 启用训练期间的验证，允许定期在独立数据集上评估模型性能。 |
| plots | False | 生成并保存训练和验证度量的图表以及预测示例，提供模型性能和学习进展的可视化洞察。 |
YOLO 数据增强
官网地址：数据增强参数
| Argument | Type | Default | Range | Description |
|---|---|---|---|---|
| hsv_h | float | 0.015 | 0.0 - 1.0 | 调整图像的色调，引入色彩变化。有助于模型在不同光照条件下泛化。 |
| hsv_s | float | 0.7 | 0.0 - 1.0 | 通过分数改变图像的饱和度，影响色彩强度。对于模拟不同环境条件有用。 |
| hsv_v | float | 0.4 | 0.0 - 1.0 | 通过分数修改图像的明度（亮度），帮助模型在各种光照条件下表现良好。 |
| degrees | float | 0.0 | -180 - +180 | 在指定的角度范围内随机旋转图像，提高模型识别不同方向物体的能力。 |
| translate | float | 0.1 | 0.0 - 1.0 | 按照图像大小的分数水平和垂直平移图像，有助于学习检测部分可见的物体。 |
| scale | float | 0.5 | >=0.0 | 通过增益因子缩放图像，模拟不同距离下的物体。 |
| shear | float | 0.0 | -180 - +180 | 通过指定角度剪切图像，模仿从不同角度观察物体的效果。 |
| perspective | float | 0.0 | 0.0 - 0.001 | 对图像应用随机透视变换，增强模型理解三维空间中物体的能力。 |
| flipud | float | 0.0 | 0.0 - 1.0 | 以指定概率上下翻转图像，增加数据多样性，不影响物体特征。 |
| fliplr | float | 0.5 | 0.0 - 1.0 | 以指定概率左右翻转图像，对于学习对称物体和增加数据集多样性有用。 |
| bgr | float | 0.0 | 0.0 - 1.0 | 以指定概率将图像通道从RGB切换到BGR，对于增加对错误通道顺序的鲁棒性有用。 |
| mosaic | float | 1.0 | 0.0 - 1.0 | 将四个训练图像组合成一个，模拟不同的场景构成和物体互动。对于复杂场景理解非常有效。 |
| mixup | float | 0.0 | 0.0 - 1.0 | 混合两个图像及其标签，创建复合图像。通过引入标签噪声和视觉变化，增强模型泛化能力。 |
| copy_paste | float | 0.0 | 0.0 - 1.0 | 从一个图像复制物体并粘贴到另一个图像上，对于增加物体实例和学习物体遮挡有用。 |
| auto_augment | str | randaugment | - | 自动应用预定义的增强策略（randaugment, autoaugment, augmix），通过多样化视觉特征优化分类任务。 |
| erasing | float | 0.4 | 0.0 - 0.9 | 在分类训练期间随机擦除图像的一部分，鼓励模型关注于不太明显的特征以进行识别。 |
| crop_fraction | float | 1.0 | 0.1 - 1.0 | 将分类图像裁剪为其大小的分数，强调中心特征，适应物体尺度，减少背景干扰。 |
