yolov11hyp.yaml

### YOLOv11 Hyperparameter Configuration File Overview YOLO (You Only Look Once) models, including the hypothetical YOLOv11 version, utilize configuration files to define hyperparameters that control various aspects of training and model performance. For a typical YOLO configuration file like `hyp.yaml`, parameters are organized into categories such as data augmentation settings, loss function weights, learning rate schedules, etc. #### Data Augmentation Parameters Data augmentation is crucial for improving generalization during training: - **mosaic**: Probability of applying mosaic augmentation which combines multiple images together. - **mixup**: Probability of using mix-up augmentation where two images are blended with each other[^1]. These augmentations help increase dataset diversity without requiring additional labeled samples. #### Loss Function Weights Adjusting these can influence how much emphasis should be placed on different types of errors within the network's predictions: - **box**: Weight applied to bounding box regression losses. - **cls_pw / obj_pw**: Positive weight factors used specifically when calculating classification or objectness confidence scores respectively; useful in addressing class imbalance issues by adjusting relative importance between positive vs negative examples[^2]. For instance, increasing `cls_pw` would give more attention towards correctly identifying rare classes over common ones. #### Learning Rate Schedule & Optimization Settings Proper tuning here ensures efficient convergence while avoiding overshooting optimal points: - **lr0**: Initial learning rate value before any decay occurs. - **lrf**: Final fraction multiplier reducing initial LR at end-of-training phase[^3]. Additionally, momentum-related terms may also appear depending upon optimizer choice but aren't explicitly mentioned above. ```yaml # Example snippet from hyp.yaml showing some key-value pairs discussed earlier train: lr0: 0.01 # Base learning rate lrf: 0.1 # Final learning rate factor loss: box: 0.05 cls_pw: 1.0 obj_pw: 1.0 augment: mosaic: 1.0 mixup: 0.2 ```