Validation Args - Ultralytics

Argument	Type	Default	Description
`data`	`str`	`None`	Specifies the path to the dataset YAML (e.g., `coco8.yaml`), which should include the path to the validation data. Classification instead takes a dataset directory or a built-in dataset name (e.g., `imagenet10`).
`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.
`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.
`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.
`conf`	`float`	`0.001`	Sets the minimum confidence threshold for detections. Lower values increase recall but may introduce more false positives. Precision-recall curves default to `0.001`; detection confusion matrices use an explicit `conf` value, or `0.25` when it is omitted. Summary precision and recall use the max-F1 confidence, so they may differ from values derived from `confusion_matrix.png`. Defaults to `0.01` for OBB validation to reduce memory usage.
`iou`	`float`	`0.7`	Sets the Intersection Over Union threshold for Non-Maximum Suppression. Controls duplicate detection elimination.
`max_det`	`int`	`300`	Limits the maximum number of detections per image. Useful in dense scenes to prevent excessive detections and manage computational resources.
`quantize`	`int` or `str`	`None`	Validation precision: `16`/`"fp16"` enables FP16 validation on supported GPUs; `32`/`"fp32"`/unset is FP32. INT8/PTQ quantization is configured during export, then used by validating the exported model. Replaces the deprecated `half` flag.
`device`	`str`	`None`	Specifies the device for validation (`cpu`, `cuda:0`, `npu`, `npu:0`, etc.). When `None`, automatically selects the best available device. Multiple CUDA devices can be specified with comma separation.
`dnn`	`bool`	`False`	If `True`, uses the OpenCV DNN module for ONNX model inference, offering an alternative to PyTorch inference methods.
`plots`	`bool`	`True`	When set to `True`, generates and saves plots of predictions versus ground truth, confusion matrices, and PR curves for visual evaluation of model performance.
`classes`	`list[int]`	`None`	Specifies a list of class IDs to evaluate. Useful for filtering out and focusing only on certain classes during evaluation.
`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. Ignored for `depth` validation, which stretches every image to a fixed `imgsz` square instead of padding.
`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.
`project`	`str`	`None`	Name of the project directory where validation outputs are saved. Helps organize results from different experiments or models.
`name`	`str`	`None`	Name of the validation run. Used for creating a subdirectory within the project folder, where validation logs and outputs are stored.
`verbose`	`bool`	`True`	If `True`, displays detailed information during the validation process, including per-class metrics, batch progress, and additional debugging information.
`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.
`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.
`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.
`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. Only available for Ultralytics PyTorch models.
`agnostic_nms`	`bool`	`False`	Enables class-agnostic Non-Maximum Suppression, suppressing lower-scoring overlapping boxes regardless of their predicted class. Useful for instance-focused applications. For end-to-end models (YOLO26, YOLOv10), this only prevents the same detection from appearing with multiple class labels (IoU=1.0 duplicates) and does not perform IoU-threshold-based suppression between distinct boxes.
`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.
`visualize`	`bool`	`False`	Visualizes the ground truths, true positives, false positives, and false negatives for each image. Useful for debugging and model interpretation.
`show_labels`	`bool`	`True`	Displays class labels in validation visualizations when `visualize=True`. Set to `False` for a cleaner view of matches and errors.
`show_conf`	`bool`	`True`	Displays confidence scores in validation visualizations when `visualize=True`. Set to `False` for a cleaner view of matches and errors.
`compile`	`bool` or `str`	`False`	Enables PyTorch 2.x `torch.compile` graph compilation with `backend='inductor'`. Accepts `True` → `"default"`, `False` → disables, or a string mode such as `"default"`, `"reduce-overhead"`, `"max-autotune-no-cudagraphs"`. Falls back to eager with a warning if unsupported.
`channels_last`	`bool`	`None`	Uses the channels_last (NHWC) memory format for native PyTorch validation. `None` automatically enables it on oneDNN-enabled Linux and Windows x86 CPUs with PyTorch 1.13 or newer, `False` disables it, and `True` requests it on supported x86 CPU or CUDA devices. ARM64, MPS, older PyTorch versions, CPUs without oneDNN, and exported formats remain unchanged; validation during training keeps the training model layout.
`end2end`	`bool`	`None`	Overrides the end-to-end mode in YOLO models that support NMS-free inference (YOLO26, YOLOv10). Setting it to `False` lets you run validation using the traditional NMS pipeline, additionally allowing you to make use of the `iou` argument.