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.
115 lines
9.9 KiB
115 lines
9.9 KiB
--- |
|
comments: true |
|
description: Discover YOLOv5u, a boosted version of the YOLOv5 model featuring an improved accuracy-speed tradeoff and numerous pre-trained models for various object detection tasks. |
|
keywords: YOLOv5u, object detection, pre-trained models, Ultralytics, Inference, Validation, YOLOv5, YOLOv8, anchor-free, objectness-free, real-time applications, machine learning |
|
--- |
|
|
|
# YOLOv5 |
|
|
|
## Overview |
|
|
|
YOLOv5u represents an advancement in object detection methodologies. Originating from the foundational architecture of the [YOLOv5](https://github.com/ultralytics/yolov5) model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the [YOLOv8](./yolov8.md) models. This adaptation refines the model's architecture, leading to an improved accuracy-speed tradeoff in object detection tasks. Given the empirical results and its derived features, YOLOv5u provides an efficient alternative for those seeking robust solutions in both research and practical applications. |
|
|
|
 |
|
|
|
## Key Features |
|
|
|
- **Anchor-free Split Ultralytics Head:** Traditional object detection models rely on predefined anchor boxes to predict object locations. However, YOLOv5u modernizes this approach. By adopting an anchor-free split Ultralytics head, it ensures a more flexible and adaptive detection mechanism, consequently enhancing the performance in diverse scenarios. |
|
|
|
- **Optimized Accuracy-Speed Tradeoff:** Speed and accuracy often pull in opposite directions. But YOLOv5u challenges this tradeoff. It offers a calibrated balance, ensuring real-time detections without compromising on accuracy. This feature is particularly invaluable for applications that demand swift responses, such as autonomous vehicles, robotics, and real-time video analytics. |
|
|
|
- **Variety of Pre-trained Models:** Understanding that different tasks require different toolsets, YOLOv5u provides a plethora of pre-trained models. Whether you're focusing on Inference, Validation, or Training, there's a tailor-made model awaiting you. This variety ensures you're not just using a one-size-fits-all solution, but a model specifically fine-tuned for your unique challenge. |
|
|
|
## Supported Tasks |
|
|
|
| Model Type | Pre-trained Weights | Task | |
|
|------------|-----------------------------------------------------------------------------------------------------------------------------|-----------| |
|
| YOLOv5u | `yolov5nu`, `yolov5su`, `yolov5mu`, `yolov5lu`, `yolov5xu`, `yolov5n6u`, `yolov5s6u`, `yolov5m6u`, `yolov5l6u`, `yolov5x6u` | Detection | |
|
|
|
## Supported Modes |
|
|
|
| Mode | Supported | |
|
|------------|--------------------| |
|
| Inference | :heavy_check_mark: | |
|
| Validation | :heavy_check_mark: | |
|
| Training | :heavy_check_mark: | |
|
|
|
!!! Performance |
|
|
|
=== "Detection" |
|
|
|
| Model | YAML | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) | |
|
|---------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------| |
|
| [yolov5nu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5nu.pt) | [yolov5n.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 34.3 | 73.6 | 1.06 | 2.6 | 7.7 | |
|
| [yolov5su.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5su.pt) | [yolov5s.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 43.0 | 120.7 | 1.27 | 9.1 | 24.0 | |
|
| [yolov5mu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5mu.pt) | [yolov5m.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 49.0 | 233.9 | 1.86 | 25.1 | 64.2 | |
|
| [yolov5lu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5lu.pt) | [yolov5l.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 52.2 | 408.4 | 2.50 | 53.2 | 135.0 | |
|
| [yolov5xu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5xu.pt) | [yolov5x.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 53.2 | 763.2 | 3.81 | 97.2 | 246.4 | |
|
| | | | | | | | | |
|
| [yolov5n6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5n6u.pt) | [yolov5n6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 42.1 | 211.0 | 1.83 | 4.3 | 7.8 | |
|
| [yolov5s6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | [yolov5s6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 48.6 | 422.6 | 2.34 | 15.3 | 24.6 | |
|
| [yolov5m6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | [yolov5m6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 53.6 | 810.9 | 4.36 | 41.2 | 65.7 | |
|
| [yolov5l6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | [yolov5l6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 55.7 | 1470.9 | 5.47 | 86.1 | 137.4 | |
|
| [yolov5x6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | [yolov5x6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 56.8 | 2436.5 | 8.98 | 155.4 | 250.7 | |
|
|
|
## Usage |
|
|
|
You can use YOLOv5u for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv5u model for inference: |
|
|
|
!!! example "" |
|
|
|
This example provides simple inference code for YOLOv5. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv5 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md). |
|
|
|
=== "Python" |
|
|
|
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python: |
|
|
|
```python |
|
from ultralytics import YOLO |
|
|
|
# Load a COCO-pretrained YOLOv5n model |
|
model = YOLO('yolov5n.pt') |
|
|
|
# Display model information (optional) |
|
model.info() |
|
|
|
# Train the model on the COCO8 example dataset for 100 epochs |
|
results = model.train(data='coco8.yaml', epochs=100, imgsz=640) |
|
|
|
# Run inference with the YOLOv5n model on the 'bus.jpg' image |
|
results = model('path/to/bus.jpg') |
|
``` |
|
|
|
=== "CLI" |
|
|
|
CLI commands are available to directly run the models: |
|
|
|
```bash |
|
# Load a COCO-pretrained YOLOv5n model and train it on the COCO8 example dataset for 100 epochs |
|
yolo train model=yolov5n.pt data=coco8.yaml epochs=100 imgsz=640 |
|
|
|
# Load a COCO-pretrained YOLOv5n model and run inference on the 'bus.jpg' image |
|
yolo predict model=yolov5n.pt source=path/to/bus.jpg |
|
``` |
|
|
|
## Citations and Acknowledgements |
|
|
|
If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows: |
|
|
|
!!! note "" |
|
|
|
=== "BibTeX" |
|
```bibtex |
|
@software{yolov5, |
|
title = {Ultralytics YOLOv5}, |
|
author = {Glenn Jocher}, |
|
year = {2020}, |
|
version = {7.0}, |
|
license = {AGPL-3.0}, |
|
url = {https://github.com/ultralytics/yolov5}, |
|
doi = {10.5281/zenodo.3908559}, |
|
orcid = {0000-0001-5950-6979} |
|
} |
|
``` |
|
|
|
Special thanks to Glenn Jocher and the Ultralytics team for their work on developing and maintaining the YOLOv5 and YOLOv5u models.
|
|
|