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ultralytics 8.1.16 OBB ConfusionMatrix support (#8299)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2024-02-19 23:59:24 +08:00 • committed by GitHub
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docs/en/modes/train.md
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@ -181,7 +181,7 @@ Training settings for YOLO models refer to the various hyperparameters and confi
| `data` | `None` | Path to the dataset configuration file (e.g., `coco128.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` | `50` | Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent overfitting by stopping training when performance plateaus. |
| `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. |
| `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. |

2
docs/en/usage/cfg.md
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@ -89,7 +89,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
| `data` | `None` | Path to the dataset configuration file (e.g., `coco128.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` | `50` | Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent overfitting by stopping training when performance plateaus. |
| `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. |
| `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. |