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129 lines
8.4 KiB
Markdown
129 lines
8.4 KiB
Markdown
---
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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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| Key | Default Value | Description |
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|---------------|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `data` | `None` | The path to the dataset configuration file (e.g., `coco128.yaml`). This file specifies the dataset's structure, including the classes, train, and validation set paths. |
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| `imgsz` | `640` | The input image size as an integer. This size is used to resize images during validation, impacting detection accuracy and inference speed. |
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| `batch` | `16` | The number of images processed in each batch. A larger batch size can speed up validation but requires more memory. Use `-1` for AutoBatch to automatically adjust based on available memory. |
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| `save_json` | `False` | If set to `True`, validation results are saved in a JSON format, useful for further analysis or submission to evaluation servers. |
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| `save_hybrid` | `False` | When `True`, saves a hybrid version of labels combining ground truth with model predictions. This can be useful for visualizing model performance or training enhancements. |
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| `conf` | `0.001` | The minimum confidence threshold for considering detections. Increasing this value may reduce false positives but could also miss less confident detections. |
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| `iou` | `0.6` | The Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Higher values result in fewer detections by eliminating more overlapping boxes. |
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| `max_det` | `300` | The maximum number of detections allowed per image. Useful for limiting outputs in images with many objects. |
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| `half` | `True` | Enables half precision (FP16) to speed up validation on compatible hardware without significantly affecting accuracy. |
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| `device` | `None` | Specifies the computation device, such as a specific GPU (`cuda:0`) or CPU (`cpu`). This setting allows for model validation on different hardware configurations. |
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| `dnn` | `False` | If `True`, uses OpenCV's DNN module for ONNX model inference. This option can be beneficial for environments where CUDA is unavailable. |
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| `plots` | `False` | Enables the generation of plots and saved images during validation, providing visual insights into model performance. |
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| `rect` | `False` | Applies rectangular inference, minimizing padding by processing images in their original aspect ratio. This can improve accuracy and speed but may require more memory. |
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| `split` | `val` | Defines the dataset split to use for validation (e.g., 'val', 'test', 'train'). This allows for flexible validation across different parts of the dataset. |
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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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