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true	YOLOv5 by Ultralytics explained. Discover the evolution of this model and its key specifications. Experience faster and more accurate object detection.

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

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.
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.
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 = {YOLOv5 by Ultralytics},
  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.

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