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Scope class badges to reference section (#23962)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
041ee5be43
commit
a90d9303c7
2 changed files with 30 additions and 29 deletions
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@ -275,8 +275,9 @@ def update_docs_soup(content: str, html_file: Path | None = None, max_title_leng
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span.insert_after(tail)
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modified = True
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highlight_labels(soup.select("main h1, main h2, main h3, main h4, main h5"))
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highlight_labels(soup.select("nav.md-nav--secondary .md-ellipsis, nav.md-nav__list .md-ellipsis"))
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if "reference" in rel_path:
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highlight_labels(soup.select("main h1, main h2, main h3, main h4, main h5"))
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highlight_labels(soup.select("nav.md-nav--secondary .md-ellipsis, nav.md-nav__list .md-ellipsis"))
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if "reference" in rel_path:
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for ellipsis in soup.select("nav.md-nav--secondary .md-ellipsis"):
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@ -75,33 +75,33 @@ By customizing these parameters, you can fine-tune the hyperparameter optimizati
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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()`.
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| Parameter | Range | Description |
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| ----------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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. |
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| `lrf` | `tune.uniform(0.01, 1.0)` | Final learning rate factor that determines how much the learning rate decreases by the end of training. |
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| `momentum` | `tune.uniform(0.7, 0.98)` | Momentum factor for the optimizer that helps accelerate training and overcome local minima. |
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| `weight_decay` | `tune.uniform(0.0, 0.001)` | Regularization parameter that prevents overfitting by penalizing large weight values. |
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| `warmup_epochs` | `tune.uniform(0.0, 5.0)` | Number of epochs with gradually increasing learning rate to stabilize early training. |
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| `warmup_momentum` | `tune.uniform(0.0, 0.95)` | Initial momentum value that gradually increases during the warmup period. |
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| `box` | `tune.uniform(1.0, 20.0)` | Weight for the bounding box loss component, balancing localization accuracy in the model. |
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| `cls` | `tune.uniform(0.1, 4.0)` | Weight for the classification loss component, balancing class prediction accuracy in the model. |
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| `dfl` | `tune.uniform(0.4, 12.0)` | Weight for the Distribution Focal Loss component, emphasizing precise bounding box localization. |
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| `hsv_h` | `tune.uniform(0.0, 0.1)` | Hue augmentation range that introduces color variability to help the model generalize. |
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| `hsv_s` | `tune.uniform(0.0, 0.9)` | Saturation augmentation range that varies color intensity to improve robustness. |
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| `hsv_v` | `tune.uniform(0.0, 0.9)` | Value (brightness) augmentation range that helps the model perform under various lighting conditions. |
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| `degrees` | `tune.uniform(0.0, 45.0)` | Rotation augmentation range in degrees, improving recognition of rotated objects. |
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| `translate` | `tune.uniform(0.0, 0.9)` | Translation augmentation range that shifts images horizontally and vertically. |
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| `scale` | `tune.uniform(0.0, 0.95)` | Scaling augmentation range that simulates objects at different distances. |
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| `shear` | `tune.uniform(0.0, 10.0)` | Shear augmentation range in degrees, simulating perspective shifts. |
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| `perspective` | `tune.uniform(0.0, 0.001)` | Perspective augmentation range that simulates 3D viewpoint changes. |
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| `flipud` | `tune.uniform(0.0, 1.0)` | Vertical flip augmentation probability, increasing dataset diversity. |
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| `fliplr` | `tune.uniform(0.0, 1.0)` | Horizontal flip augmentation probability, useful for symmetrical objects. |
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| `bgr` | `tune.uniform(0.0, 1.0)` | BGR channel swap augmentation probability, helping with color invariance. |
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| `mosaic` | `tune.uniform(0.0, 1.0)` | Mosaic augmentation probability that combines four images into one training sample. |
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| `mixup` | `tune.uniform(0.0, 1.0)` | Mixup augmentation probability that blends two images and their labels together. |
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| `cutmix` | `tune.uniform(0.0, 1.0)` | Cutmix augmentation probability that combines image regions while maintaining local features. |
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| `copy_paste` | `tune.uniform(0.0, 1.0)` | Copy-paste augmentation probability that transfers objects between images to increase instance diversity. |
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| `close_mosaic` | `tune.uniform(0.0, 10.0)` | Disables mosaic in the last N epochs to stabilize training before completion. |
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| Parameter | Range | Description |
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| ----------------- | -------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
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| `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. |
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| `lrf` | `tune.uniform(0.01, 1.0)` | Final learning rate factor that determines how much the learning rate decreases by the end of training. |
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| `momentum` | `tune.uniform(0.7, 0.98)` | Momentum factor for the optimizer that helps accelerate training and overcome local minima. |
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| `weight_decay` | `tune.uniform(0.0, 0.001)` | Regularization parameter that prevents overfitting by penalizing large weight values. |
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| `warmup_epochs` | `tune.uniform(0.0, 5.0)` | Number of epochs with gradually increasing learning rate to stabilize early training. |
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| `warmup_momentum` | `tune.uniform(0.0, 0.95)` | Initial momentum value that gradually increases during the warmup period. |
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| `box` | `tune.uniform(1.0, 20.0)` | Weight for the bounding box loss component, balancing localization accuracy in the model. |
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| `cls` | `tune.uniform(0.1, 4.0)` | Weight for the classification loss component, balancing class prediction accuracy in the model. |
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| `dfl` | `tune.uniform(0.4, 12.0)` | Weight for the Distribution Focal Loss component, emphasizing precise bounding box localization. |
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| `hsv_h` | `tune.uniform(0.0, 0.1)` | Hue augmentation range that introduces color variability to help the model generalize. |
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| `hsv_s` | `tune.uniform(0.0, 0.9)` | Saturation augmentation range that varies color intensity to improve robustness. |
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| `hsv_v` | `tune.uniform(0.0, 0.9)` | Value (brightness) augmentation range that helps the model perform under various lighting conditions. |
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| `degrees` | `tune.uniform(0.0, 45.0)` | Rotation augmentation range in degrees, improving recognition of rotated objects. |
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| `translate` | `tune.uniform(0.0, 0.9)` | Translation augmentation range that shifts images horizontally and vertically. |
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| `scale` | `tune.uniform(0.0, 0.95)` | Scaling augmentation range that simulates objects at different distances. |
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| `shear` | `tune.uniform(0.0, 10.0)` | Shear augmentation range in degrees, simulating perspective shifts. |
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| `perspective` | `tune.uniform(0.0, 0.001)` | Perspective augmentation range that simulates 3D viewpoint changes. |
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| `flipud` | `tune.uniform(0.0, 1.0)` | Vertical flip augmentation probability, increasing dataset diversity. |
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| `fliplr` | `tune.uniform(0.0, 1.0)` | Horizontal flip augmentation probability, useful for symmetrical objects. |
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| `bgr` | `tune.uniform(0.0, 1.0)` | BGR channel swap augmentation probability, helping with color invariance. |
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| `mosaic` | `tune.uniform(0.0, 1.0)` | Mosaic augmentation probability that combines four images into one training sample. |
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| `mixup` | `tune.uniform(0.0, 1.0)` | Mixup augmentation probability that blends two images and their labels together. |
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| `cutmix` | `tune.uniform(0.0, 1.0)` | Cutmix augmentation probability that combines image regions while maintaining local features. |
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| `copy_paste` | `tune.uniform(0.0, 1.0)` | Copy-paste augmentation probability that transfers objects between images to increase instance diversity. |
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| `close_mosaic` | `tune.uniform(0.0, 10.0)` | Disables mosaic in the last N epochs to stabilize training before completion. |
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## Custom Search Space Example
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