6.5 KiB
| comments | description | keywords |
|---|---|---|
| true | 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. | YOLOv5u, object detection, pre-trained models, Ultralytics, Inference, Validation, YOLOv5, YOLOv8, anchor-free, objectness-free, real-time applications, machine learning |
YOLOv5
Overview
YOLOv5u is an enhanced version of the YOLOv5 object detection model from Ultralytics. This iteration incorporates the anchor-free, objectness-free split head that is featured in the YOLOv8 models. Although it maintains the same backbone and neck architecture as YOLOv5, YOLOv5u provides an improved accuracy-speed tradeoff for object detection tasks, making it a robust choice for numerous applications.
Key Features
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Anchor-free Split Ultralytics Head: YOLOv5u replaces the conventional anchor-based detection head with an anchor-free split Ultralytics head, boosting performance in object detection tasks.
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Optimized Accuracy-Speed Tradeoff: By delivering a better balance between accuracy and speed, YOLOv5u is suitable for a diverse range of real-time applications, from autonomous driving to video surveillance.
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Variety of Pre-trained Models: YOLOv5u includes numerous pre-trained models for tasks like Inference, Validation, and Training, providing the flexibility to tackle various object detection challenges.
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 | ✔️ |
| Validation | ✔️ |
| Training | ✔️ |
!!! Performance
=== "Detection"
| Model | 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](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5nu.pt) | 640 | 34.3 | 73.6 | 1.06 | 2.6 | 7.7 |
| [YOLOv5su](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5su.pt) | 640 | 43.0 | 120.7 | 1.27 | 9.1 | 24.0 |
| [YOLOv5mu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5mu.pt) | 640 | 49.0 | 233.9 | 1.86 | 25.1 | 64.2 |
| [YOLOv5lu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5lu.pt) | 640 | 52.2 | 408.4 | 2.50 | 53.2 | 135.0 |
| [YOLOv5xu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5xu.pt) | 640 | 53.2 | 763.2 | 3.81 | 97.2 | 246.4 |
| | | | | | | |
| [YOLOv5n6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5n6u.pt) | 1280 | 42.1 | 211.0 | 1.83 | 4.3 | 7.8 |
| [YOLOv5s6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | 1280 | 48.6 | 422.6 | 2.34 | 15.3 | 24.6 |
| [YOLOv5m6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | 1280 | 53.6 | 810.9 | 4.36 | 41.2 | 65.7 |
| [YOLOv5l6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | 1280 | 55.7 | 1470.9 | 5.47 | 86.1 | 137.4 |
| [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 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:
from ultralytics import YOLO
# Load the model
model = YOLO('yolov5n.pt') # load a pretrained model
# Perform inference
results = model('image.jpg')
# Print the results
results.print()
Citations and Acknowledgments
If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows:
@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.