ultralytics 8.2.29 new fractional AutoBatch feature (#13446)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/modes/train.md

@@ -182,7 +182,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
 | `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 for training, indicating how many images are processed before the model's internal parameters are updated. AutoBatch (`batch=-1`) dynamically adjusts the batch size based on GPU memory availability.    |
+| `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.                                                     |
@@ -226,6 +226,14 @@ The training settings for YOLO models encompass various hyperparameters and conf
 | `val`             | `True`   | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset.                                                                                                     |
 | `plots`           | `False`  | Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression.                                             |
 
+!!! info "Note on Batch-size Settings"
+    
+    The `batch` argument can be configured in three ways:
+    
+    - **Fixed Batch Size**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
+    - **Auto Mode (60% GPU Memory)**: Use `batch=-1` to automatically adjust batch size for approximately 60% CUDA memory utilization.
+    - **Auto Mode with Utilization Fraction**: Set a fraction value (e.g., `batch=0.70`) to adjust batch size based on the specified fraction of GPU memory usage.
+
 ## Augmentation Settings and Hyperparameters
 
 Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the training data, helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:

docs/en/usage/cfg.md

@@ -91,7 +91,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
 | `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 for training, indicating how many images are processed before the model's internal parameters are updated. AutoBatch (`batch=-1`) dynamically adjusts the batch size based on GPU memory availability.    |
+| `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.                                                     |
@@ -135,6 +135,14 @@ The training settings for YOLO models encompass various hyperparameters and conf
 | `val`             | `True`   | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset.                                                                                                     |
 | `plots`           | `False`  | Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression.                                             |
 
+!!! info "Note on Batch-size Settings"
+    
+    The `batch` argument can be configured in three ways:
+    
+    - **Fixed Batch Size**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
+    - **Auto Mode (60% GPU Memory)**: Use `batch=-1` to automatically adjust batch size for approximately 60% CUDA memory utilization.
+    - **Auto Mode with Utilization Fraction**: Set a fraction value (e.g., `batch=0.70`) to adjust batch size based on the specified fraction of GPU memory usage.
+
 [Train Guide](../modes/train.md){ .md-button }
 
 ## Predict Settings