You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
128 lines
8.0 KiB
128 lines
8.0 KiB
--- |
|
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 |
|
|
|
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="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. |
|
|
|
<p align="center"> |
|
<br> |
|
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/j8uQc0qB91s?start=47" |
|
title="YouTube video player" frameborder="0" |
|
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
|
allowfullscreen> |
|
</iframe> |
|
<br> |
|
<strong>Watch:</strong> Ultralytics Modes Tutorial: Validation |
|
</p> |
|
|
|
## 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 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. |
|
|
|
!!! 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., `coco128.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 |
|
```
|
|
|