Models
YOLOv7 Instance Segmentation vs. OneFormer

YOLOv7 Instance Segmentation vs. OneFormer

Both YOLOv7 Instance Segmentation and OneFormer are commonly used in computer vision projects. Below, we compare and contrast YOLOv7 Instance Segmentation and OneFormer.

Models

icon-model

YOLOv7 Instance Segmentation

YOLOv7 Instance Segmentation lets you perform segmentation tasks with the YOLOv7 model.
Learn more about YOLOv7 Instance Segmentation
icon-model

OneFormer

OneFormer is a state-of-the-art multi-task image segmentation framework that is implemented using transformers.
Learn more about OneFormer
Model Type
Instance Segmentation
--
Instance Segmentation
--
Model Features
Item 1 Info
Item 2 Info
Architecture
YOLO
--
Transformers
--
Frameworks
PyTorch
--
PyTorch
--
Annotation Format
Instance Segmentation
Instance Segmentation
GitHub
--
View Repo
--
View Repo
GitHub Stars
12.5k+
--
1.3k+
--
License
GPL-3.0
--
MIT
--
Paper
--
View Paper
--
View Paper
Training Notebook
Train on Colab
--
Train on Colab
--
Deploy Model
--
rocket icon
Deploy with Roboflow
--
rocket icon
Deploy with Roboflow
Compare Alternatives
--
Compare with...
--
Compare with...

Compare YOLOv7 Instance Segmentation and OneFormer with Autodistill

Compare YOLOv7 Instance Segmentation vs. OneFormer

Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset.

COCO can detect 80 common objects, including cats, cell phones, and cars.