--- comments: true description: Guide for Validating YOLOv8 Models. Learn how to evaluate the performance of your YOLO models using validation settings and metrics with Python and CLI examples. keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperparameters, accuracy, metrics, Python, CLI --- # Model Validation with Ultralytics YOLO Ultralytics YOLO ecosystem and integrations ## Introduction Validation is a critical step in the machine learning pipeline, allowing you to assess the quality of your trained models. Val mode in Ultralytics YOLOv8 provides a robust suite of tools and metrics for evaluating the performance of your object detection models. This guide serves as a complete resource for understanding how to effectively use the Val mode to ensure that your models are both accurate and reliable.



Watch: Ultralytics Modes Tutorial: Validation

## Why Validate with Ultralytics YOLO? Here's why using YOLOv8's Val mode is advantageous: - **Precision:** Get accurate metrics like mAP50, mAP75, and mAP50-95 to comprehensively evaluate your model. - **Convenience:** Utilize built-in features that remember training settings, simplifying the validation process. - **Flexibility:** Validate your model with the same or different datasets and image sizes. - **Hyperparameter Tuning:** Use validation metrics to fine-tune your model for better performance. ### Key Features of Val Mode These are the notable functionalities offered by YOLOv8's Val mode: - **Automated Settings:** Models remember their training configurations for straightforward validation. - **Multi-Metric Support:** Evaluate your model based on a range of accuracy metrics. - **CLI and Python API:** Choose from command-line interface or Python API based on your preference for validation. - **Data Compatibility:** Works seamlessly with datasets used during the training phase as well as custom datasets. !!! Tip "Tip" * YOLOv8 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolov8n.pt` or `model('yolov8n.pt').val()` ## Usage Examples Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments. !!! Example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO('yolov8n.pt') # load an official model model = YOLO('path/to/best.pt') # load a custom model # Validate the model metrics = model.val() # no arguments needed, dataset and settings remembered metrics.box.map # map50-95 metrics.box.map50 # map50 metrics.box.map75 # map75 metrics.box.maps # a list contains map50-95 of each category ``` === "CLI" ```bash yolo detect val model=yolov8n.pt # val official model yolo detect val model=path/to/best.pt # val custom model ``` ## Arguments for YOLO Model Validation When validating YOLO models, several arguments can be fine-tuned to optimize the evaluation process. These arguments control aspects such as input image size, batch processing, and performance thresholds. Below is a detailed breakdown of each argument to help you customize your validation settings effectively. | Argument | Type | Default | Description | |---------------|---------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------| | `data` | `str` | `None` | Specifies the path to the dataset configuration file (e.g., `coco8.yaml`). This file includes paths to validation data, class names, and number of classes. | | `imgsz` | `int` | `640` | Defines the size of input images. All images are resized to this dimension before processing. | | `batch` | `int` | `16` | Sets the number of images per batch. Use `-1` for AutoBatch, which automatically adjusts based on GPU memory availability. | | `save_json` | `bool` | `False` | If `True`, saves the results to a JSON file for further analysis or integration with other tools. | | `save_hybrid` | `bool` | `False` | If `True`, saves a hybrid version of labels that combines original annotations with additional model predictions. | | `conf` | `float` | `0.001` | Sets the minimum confidence threshold for detections. Detections with confidence below this threshold are discarded. | | `iou` | `float` | `0.6` | Sets the Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Helps in reducing duplicate detections. | | `max_det` | `int` | `300` | Limits the maximum number of detections per image. Useful in dense scenes to prevent excessive detections. | | `half` | `bool` | `True` | Enables half-precision (FP16) computation, reducing memory usage and potentially increasing speed with minimal impact on accuracy. | | `device` | `str` | `None` | Specifies the device for validation (`cpu`, `cuda:0`, etc.). Allows flexibility in utilizing CPU or GPU resources. | | `dnn` | `bool` | `False` | If `True`, uses the OpenCV DNN module for ONNX model inference, offering an alternative to PyTorch inference methods. | | `plots` | `bool` | `False` | When set to `True`, generates and saves plots of predictions versus ground truth for visual evaluation of the model's performance. | | `rect` | `bool` | `False` | If `True`, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency. | | `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. | Each of these settings plays a vital role in the validation process, allowing for a customizable and efficient evaluation of YOLO models. Adjusting these parameters according to your specific needs and resources can help achieve the best balance between accuracy and performance. ### Example Validation with Arguments The below examples showcase YOLO model validation with custom arguments in Python and CLI. !!! Example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO('yolov8n.pt') # Customize validation settings validation_results = model.val(data='coco8.yaml', imgsz=640, batch=16, conf=0.25, iou=0.6, device='0') ``` === "CLI" ```bash yolo val model=yolov8n.pt data=coco8.yaml imgsz=640 batch=16 conf=0.25 iou=0.6 device=0 ```