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YOLOv5u

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
Model size
(pixels)		 mAPval
50-95		 Speed
CPU ONNX
(ms)		 Speed
A100 TensorRT
(ms)		 params
(M)		 FLOPs
(B)
YOLOv5nu 640 34.3 73.6 1.06 2.6 7.7
YOLOv5su 640 43.0 120.7 1.27 9.1 24.0
YOLOv5mu 640 49.0 233.9 1.86 25.1 64.2
YOLOv5lu 640 52.2 408.4 2.50 53.2 135.0
YOLOv5xu 640 53.2 763.2 3.81 97.2 246.4
YOLOv5n6u 1280 42.1 - - 4.3 7.8
YOLOv5s6u 1280 48.6 - - 15.3 24.6
YOLOv5m6u 1280 53.6 - - 41.2 65.7
YOLOv5l6u 1280 55.7 - - 86.1 137.4
YOLOv5x6u 1280 56.8 - - 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.


Created 2023-05-01, Updated 2023-05-22
Authors: Glenn Jocher (4)

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