26 lines
7.2 KiB
Markdown
26 lines
7.2 KiB
Markdown
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| Argument | Type | Default | Description |
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| -------------- | ----------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `data` | `str` | `None` | Specifies the path to the dataset configuration file (e.g., `coco8.yaml`). This file includes paths to [validation data](https://www.ultralytics.com/glossary/validation-data), class names, and number of classes. |
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| `imgsz` | `int` | `640` | Defines the size of input images. All images are resized to this dimension before processing. Larger sizes may improve accuracy for small objects but increase computation time. |
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| `batch` | `int` | `16` | Sets the number of images per batch. Higher values utilize GPU memory more efficiently but require more VRAM. Adjust based on available hardware resources. |
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| `save_json` | `bool` | `False` | If `True`, saves the results to a JSON file for further analysis, integration with other tools, or submission to evaluation servers like COCO. |
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| `conf` | `float` | `0.001` | Sets the minimum confidence threshold for detections. Lower values increase recall but may introduce more false positives. Used during [validation](https://docs.ultralytics.com/modes/val/) to compute precision-recall curves. |
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| `iou` | `float` | `0.7` | Sets the [Intersection Over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) threshold for [Non-Maximum Suppression](https://www.ultralytics.com/glossary/non-maximum-suppression-nms). Controls duplicate detection elimination. |
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| `max_det` | `int` | `300` | Limits the maximum number of detections per image. Useful in dense scenes to prevent excessive detections and manage computational resources. |
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| `half` | `bool` | `True` | Enables half-[precision](https://www.ultralytics.com/glossary/precision) (FP16) computation, reducing memory usage and potentially increasing speed with minimal impact on [accuracy](https://www.ultralytics.com/glossary/accuracy). |
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| `device` | `str` | `None` | Specifies the device for validation (`cpu`, `cuda:0`, etc.). When `None`, automatically selects the best available device. Multiple CUDA devices can be specified with comma separation. |
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| `dnn` | `bool` | `False` | If `True`, uses the [OpenCV](https://www.ultralytics.com/glossary/opencv) DNN module for ONNX model inference, offering an alternative to [PyTorch](https://www.ultralytics.com/glossary/pytorch) inference methods. |
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| `plots` | `bool` | `False` | When set to `True`, generates and saves plots of predictions versus ground truth, confusion matrices, and PR curves for visual evaluation of model performance. |
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| `classes` | `list[int]` | `None` | Specifies a list of class IDs to train on. Useful for filtering out and focusing only on certain classes during evaluation. |
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| `rect` | `bool` | `True` | If `True`, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency by processing images in their original aspect ratio. |
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| `split` | `str` | `'val'` | Determines the dataset split to use for validation (`val`, `test`, or `train`). Allows flexibility in choosing the data segment for performance evaluation. |
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| `project` | `str` | `None` | Name of the project directory where validation outputs are saved. Helps organize results from different experiments or models. |
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| `name` | `str` | `None` | Name of the validation run. Used for creating a subdirectory within the project folder, where validation logs and outputs are stored. |
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| `verbose` | `bool` | `False` | If `True`, displays detailed information during the validation process, including per-class metrics, batch progress, and additional debugging information. |
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| `save_txt` | `bool` | `False` | If `True`, saves detection results in text files, with one file per image, useful for further analysis, custom post-processing, or integration with other systems. |
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| `save_conf` | `bool` | `False` | If `True`, includes confidence values in the saved text files when `save_txt` is enabled, providing more detailed output for analysis and filtering. |
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| `workers` | `int` | `8` | Number of worker threads for data loading. Higher values can speed up data preprocessing but may increase CPU usage. Setting to 0 uses main thread, which can be more stable in some environments. |
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| `augment` | `bool` | `False` | Enables test-time augmentation (TTA) during validation, potentially improving detection accuracy at the cost of inference speed by running inference on transformed versions of the input. |
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| `agnostic_nms` | `bool` | `False` | Enables class-agnostic [Non-Maximum Suppression](https://www.ultralytics.com/glossary/non-maximum-suppression-nms), which merges overlapping boxes regardless of their predicted class. Useful for instance-focused applications. |
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| `single_cls` | `bool` | `False` | Treats all classes as a single class during validation. Useful for evaluating model performance on binary detection tasks or when class distinctions aren't important. |
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