MT-YOLOv6 is a YOLO based model released in 2022.
Here is an overview of the
model:
The YOLOv6 repository was published June 2022 by Meituan, and it claims new state-of-the-art performance on the COCO dataset benchmark. We'll leave it to the community to determine if this name is the best representation for the architecture.
https://blog.roboflow.com/yolov6/
In any case, it's clear MT-YOLOv6 (hereafter YOLOv6 for brevity) is popular. In a couple short weeks, the repo has attracted over 2,000+ stars and 300+ forks.
YOLOv6 claims to set a new state-of-the-art performance on the COCO dataset benchmark. As the authors detail, YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset (with 520 FPS on T4 using TensorRT FP16 for bs32 inference).
(For point of comparison, YOLOv5-s achieves 37.4 mAP @ 0.95% on the same COCO benchmark.)
The YOLOv6 repository authors published the below evaluation graphic, demonstrating YOLOv6 outperforming YOLOv5 and YOLOX at similar sizes.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
Roboflow offers a range of SDKs with which you can deploy your model to production.
MT-YOLOv6
uses the
uses the
meituan/yolov6
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
You can automatically label a dataset using
MT-YOLOv6
with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use
MT-YOLOv6
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
Join 100k developers curating high quality datasets and deploying better models with Roboflow.
Get started