diff --git a/docs/build_docs.py b/docs/build_docs.py index 9a0d437762..1a32ec0315 100644 --- a/docs/build_docs.py +++ b/docs/build_docs.py @@ -275,8 +275,9 @@ def update_docs_soup(content: str, html_file: Path | None = None, max_title_leng span.insert_after(tail) modified = True - highlight_labels(soup.select("main h1, main h2, main h3, main h4, main h5")) - highlight_labels(soup.select("nav.md-nav--secondary .md-ellipsis, nav.md-nav__list .md-ellipsis")) + if "reference" in rel_path: + highlight_labels(soup.select("main h1, main h2, main h3, main h4, main h5")) + highlight_labels(soup.select("nav.md-nav--secondary .md-ellipsis, nav.md-nav__list .md-ellipsis")) if "reference" in rel_path: for ellipsis in soup.select("nav.md-nav--secondary .md-ellipsis"): diff --git a/docs/en/integrations/ray-tune.md b/docs/en/integrations/ray-tune.md index e652c99766..da9fba54d2 100644 --- a/docs/en/integrations/ray-tune.md +++ b/docs/en/integrations/ray-tune.md @@ -75,33 +75,33 @@ By customizing these parameters, you can fine-tune the hyperparameter optimizati The following table lists the default search space parameters for hyperparameter tuning in YOLO26 with Ray Tune. Each parameter has a specific value range defined by `tune.uniform()`. -| Parameter | Range | Description | -| ----------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | -| `lr0` | `tune.uniform(1e-5, 1e-2)` | Initial learning rate that controls the step size during optimization. Higher values speed up training but may cause instability. | -| `lrf` | `tune.uniform(0.01, 1.0)` | Final learning rate factor that determines how much the learning rate decreases by the end of training. | -| `momentum` | `tune.uniform(0.7, 0.98)` | Momentum factor for the optimizer that helps accelerate training and overcome local minima. | -| `weight_decay` | `tune.uniform(0.0, 0.001)` | Regularization parameter that prevents overfitting by penalizing large weight values. | -| `warmup_epochs` | `tune.uniform(0.0, 5.0)` | Number of epochs with gradually increasing learning rate to stabilize early training. | -| `warmup_momentum` | `tune.uniform(0.0, 0.95)` | Initial momentum value that gradually increases during the warmup period. | -| `box` | `tune.uniform(1.0, 20.0)` | Weight for the bounding box loss component, balancing localization accuracy in the model. | -| `cls` | `tune.uniform(0.1, 4.0)` | Weight for the classification loss component, balancing class prediction accuracy in the model. | -| `dfl` | `tune.uniform(0.4, 12.0)` | Weight for the Distribution Focal Loss component, emphasizing precise bounding box localization. | -| `hsv_h` | `tune.uniform(0.0, 0.1)` | Hue augmentation range that introduces color variability to help the model generalize. | -| `hsv_s` | `tune.uniform(0.0, 0.9)` | Saturation augmentation range that varies color intensity to improve robustness. | -| `hsv_v` | `tune.uniform(0.0, 0.9)` | Value (brightness) augmentation range that helps the model perform under various lighting conditions. | -| `degrees` | `tune.uniform(0.0, 45.0)` | Rotation augmentation range in degrees, improving recognition of rotated objects. | -| `translate` | `tune.uniform(0.0, 0.9)` | Translation augmentation range that shifts images horizontally and vertically. | -| `scale` | `tune.uniform(0.0, 0.95)` | Scaling augmentation range that simulates objects at different distances. | -| `shear` | `tune.uniform(0.0, 10.0)` | Shear augmentation range in degrees, simulating perspective shifts. | -| `perspective` | `tune.uniform(0.0, 0.001)` | Perspective augmentation range that simulates 3D viewpoint changes. | -| `flipud` | `tune.uniform(0.0, 1.0)` | Vertical flip augmentation probability, increasing dataset diversity. | -| `fliplr` | `tune.uniform(0.0, 1.0)` | Horizontal flip augmentation probability, useful for symmetrical objects. | -| `bgr` | `tune.uniform(0.0, 1.0)` | BGR channel swap augmentation probability, helping with color invariance. | -| `mosaic` | `tune.uniform(0.0, 1.0)` | Mosaic augmentation probability that combines four images into one training sample. | -| `mixup` | `tune.uniform(0.0, 1.0)` | Mixup augmentation probability that blends two images and their labels together. | -| `cutmix` | `tune.uniform(0.0, 1.0)` | Cutmix augmentation probability that combines image regions while maintaining local features. | -| `copy_paste` | `tune.uniform(0.0, 1.0)` | Copy-paste augmentation probability that transfers objects between images to increase instance diversity. | -| `close_mosaic` | `tune.uniform(0.0, 10.0)` | Disables mosaic in the last N epochs to stabilize training before completion. | +| Parameter | Range | Description | +| ----------------- | -------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | +| `lr0` | `tune.uniform(1e-5, 1e-2)` | Initial learning rate that controls the step size during optimization. Higher values speed up training but may cause instability. | +| `lrf` | `tune.uniform(0.01, 1.0)` | Final learning rate factor that determines how much the learning rate decreases by the end of training. | +| `momentum` | `tune.uniform(0.7, 0.98)` | Momentum factor for the optimizer that helps accelerate training and overcome local minima. | +| `weight_decay` | `tune.uniform(0.0, 0.001)` | Regularization parameter that prevents overfitting by penalizing large weight values. | +| `warmup_epochs` | `tune.uniform(0.0, 5.0)` | Number of epochs with gradually increasing learning rate to stabilize early training. | +| `warmup_momentum` | `tune.uniform(0.0, 0.95)` | Initial momentum value that gradually increases during the warmup period. | +| `box` | `tune.uniform(1.0, 20.0)` | Weight for the bounding box loss component, balancing localization accuracy in the model. | +| `cls` | `tune.uniform(0.1, 4.0)` | Weight for the classification loss component, balancing class prediction accuracy in the model. | +| `dfl` | `tune.uniform(0.4, 12.0)` | Weight for the Distribution Focal Loss component, emphasizing precise bounding box localization. | +| `hsv_h` | `tune.uniform(0.0, 0.1)` | Hue augmentation range that introduces color variability to help the model generalize. | +| `hsv_s` | `tune.uniform(0.0, 0.9)` | Saturation augmentation range that varies color intensity to improve robustness. | +| `hsv_v` | `tune.uniform(0.0, 0.9)` | Value (brightness) augmentation range that helps the model perform under various lighting conditions. | +| `degrees` | `tune.uniform(0.0, 45.0)` | Rotation augmentation range in degrees, improving recognition of rotated objects. | +| `translate` | `tune.uniform(0.0, 0.9)` | Translation augmentation range that shifts images horizontally and vertically. | +| `scale` | `tune.uniform(0.0, 0.95)` | Scaling augmentation range that simulates objects at different distances. | +| `shear` | `tune.uniform(0.0, 10.0)` | Shear augmentation range in degrees, simulating perspective shifts. | +| `perspective` | `tune.uniform(0.0, 0.001)` | Perspective augmentation range that simulates 3D viewpoint changes. | +| `flipud` | `tune.uniform(0.0, 1.0)` | Vertical flip augmentation probability, increasing dataset diversity. | +| `fliplr` | `tune.uniform(0.0, 1.0)` | Horizontal flip augmentation probability, useful for symmetrical objects. | +| `bgr` | `tune.uniform(0.0, 1.0)` | BGR channel swap augmentation probability, helping with color invariance. | +| `mosaic` | `tune.uniform(0.0, 1.0)` | Mosaic augmentation probability that combines four images into one training sample. | +| `mixup` | `tune.uniform(0.0, 1.0)` | Mixup augmentation probability that blends two images and their labels together. | +| `cutmix` | `tune.uniform(0.0, 1.0)` | Cutmix augmentation probability that combines image regions while maintaining local features. | +| `copy_paste` | `tune.uniform(0.0, 1.0)` | Copy-paste augmentation probability that transfers objects between images to increase instance diversity. | +| `close_mosaic` | `tune.uniform(0.0, 10.0)` | Disables mosaic in the last N epochs to stabilize training before completion. | ## Custom Search Space Example