Product
Solutions
Resources
Pricing
Docs
Talk to SalesSign in
Models
YOLOR vs. MT-YOLOv6

Explore Models

By Task
All Models (32)Object Detection Models (19)Classification Models (7)Instance Segmentation Models (3)Semantic Segmentation Models (1)
By Feature
Foundation Vision ModelsMultimodal Vision ModelsImage Embedding ModelsReal Time Vision ModelsZero-Shot Detection ModelsOptimal for Edge Deployment

YOLOR vs. MT-YOLOv6

Both

YOLOR

and

MT-YOLOv6

are commonly used in computer vision projects. Below, we compare and contrast

YOLOR

and

MT-YOLOv6

.

  YOLOR MT-YOLOv6
Date of Release May 10, 2021 Jun 23, 2022
Model Type Object Detection Object Detection
Architecture CNN, YOLO CNN, YOLO
GitHub Stars 1800 4400

YOLOR

YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.

How to AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion Matrix

MT-YOLOv6

MT-YOLOv6 is a YOLO based model released in 2022.

How to AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion Matrix
Learn more about YOLOR
Learn more about MT-YOLOv6

Compare YOLOR to other models

MobileNet V2 Classification
MobileNet SSD v2
YOLOv3 Keras
YOLOv3 PyTorch
YOLOv4 PyTorch

Compare MT-YOLOv6 to other models

YOLOv8 Instance Segmentation
YOLOv8
YOLOv7 Instance Segmentation
OneFormer
MobileNet V2 Classification

Deploy a computer vision model today

Join 250,000 developers curating high quality datasets and deploying better models with Roboflow.

Get started