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---
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comments: true
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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.
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keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperparameters, accuracy, metrics, Python, CLI
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---
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# Model Validation with Ultralytics YOLO
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
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## Introduction
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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.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/j8uQc0qB91s?start=47"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Ultralytics Modes Tutorial: Validation
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</p>
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## Why Validate with Ultralytics YOLO?
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Here's why using YOLOv8's Val mode is advantageous:
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- **Precision:** Get accurate metrics like mAP50, mAP75, and mAP50-95 to comprehensively evaluate your model.
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- **Convenience:** Utilize built-in features that remember training settings, simplifying the validation process.
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- **Flexibility:** Validate your model with the same or different datasets and image sizes.
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- **Hyperparameter Tuning:** Use validation metrics to fine-tune your model for better performance.
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### Key Features of Val Mode
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These are the notable functionalities offered by YOLOv8's Val mode:
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- **Automated Settings:** Models remember their training configurations for straightforward validation.
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- **Multi-Metric Support:** Evaluate your model based on a range of accuracy metrics.
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- **CLI and Python API:** Choose from command-line interface or Python API based on your preference for validation.
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- **Data Compatibility:** Works seamlessly with datasets used during the training phase as well as custom datasets.
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!!! Tip "Tip"
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* 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()`
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## Usage Examples
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Validate trained YOLOv8n model accuracy on the COCO128 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.
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!!! Example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics.box.map # map50-95
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metrics.box.map50 # map50
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metrics.box.map75 # map75
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metrics.box.maps # a list contains map50-95 of each category
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```
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=== "CLI"
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```bash
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yolo detect val model=yolov8n.pt # val official model
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yolo detect val model=path/to/best.pt # val custom model
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```
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## Arguments for YOLO Model Validation
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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.
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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., `coco128.yaml`). This file includes paths to 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. |
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| `batch` | `int` | `16` | Sets the number of images per batch. Use `-1` for AutoBatch, which automatically adjusts based on GPU memory availability. |
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| `save_json` | `bool` | `False` | If `True`, saves the results to a JSON file for further analysis or integration with other tools. |
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| `save_hybrid` | `bool` | `False` | If `True`, saves a hybrid version of labels that combines original annotations with additional model predictions. |
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| `conf` | `float` | `0.001` | Sets the minimum confidence threshold for detections. Detections with confidence below this threshold are discarded. |
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| `iou` | `float` | `0.6` | Sets the Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Helps in reducing duplicate detections. |
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| `max_det` | `int` | `300` | Limits the maximum number of detections per image. Useful in dense scenes to prevent excessive detections. |
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| `half` | `bool` | `True` | Enables half-precision (FP16) computation, reducing memory usage and potentially increasing speed with minimal impact on accuracy. |
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| `device` | `str` | `None` | Specifies the device for validation (`cpu`, `cuda:0`, etc.). Allows flexibility in utilizing CPU or GPU resources. |
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| `dnn` | `bool` | `False` | If `True`, uses the OpenCV DNN module for ONNX model inference, offering an alternative to PyTorch inference methods. |
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| `plots` | `bool` | `False` | When set to `True`, generates and saves plots of predictions versus ground truth for visual evaluation of the model's performance. |
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| `rect` | `bool` | `False` | If `True`, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency. |
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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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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.
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### Example Validation with Arguments
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The below examples showcase YOLO model validation with custom arguments in Python and CLI.
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!!! Example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt')
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# Customize validation settings
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validation_results = model.val(data='coco8.yaml',
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imgsz=640,
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batch=16,
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conf=0.25,
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iou=0.6,
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device='0')
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```
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=== "CLI"
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```bash
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yolo val model=yolov8n.pt data=coco8.yaml imgsz=640 batch=16 conf=0.25 iou=0.6 device=0
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```
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