OneFormer is a new segmentation model that earned 5x state-of-the-art badges from Papers with Code. It beat former SOTA solutions — MaskFormer and Mask2Former, and it is now ranked number one in the instance, semantic and panoptic segmentation. OneFormer is based on transformers and built using Detectron2.

OneFormer is the first multi-task image segmentation framework. This means the model only needs to be trained once with universal architecture and a single dataset. Previously, even if the model scored high in all three segmentation tasks, it needed to be trained individually on the semantic, instance, or panoptic datasets.

OneFormer introduces a task-conditional joint training strategy. The model uniformly samples training examples from different ground truth domains. As a result, the model architecture is task-guided for training and task-dynamic for inference, all with a single model.

Check out YOLOv8, defining a new state-of-the-art in computer vision

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.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Model Performance

ADE20K

Method	Backbone	Crop Size	PQ	AP	mIoU (s.s)	mIoU (ms+flip)	#params	config	Checkpoint
OneFormer	Swin-L^†	640×640	48.6	35.9	57.0	57.7	219M	config	model
OneFormer	Swin-L^†	896×896	50.2	37.6	57.4	58.3	219M	config	model
OneFormer	ConvNeXt-L^†	640×640	48.7	36.2	56.6	57.4	220M	config	model
OneFormer	DiNAT-L^†	640×640	49.1	36.0	57.8	58.4	223M	config	model
OneFormer	DiNAT-L^†	896×896	50.0	36.8	58.1	58.6	223M	config	model
OneFormer	ConvNeXt-XL^†	640×640	48.9	36.3	57.4	58.8	372M	config	model

Cityscapes

Method	Backbone	PQ	AP	mIoU (s.s)	mIoU (ms+flip)	#params	config	Checkpoint
OneFormer	Swin-L^†	67.2	45.6	83.0	84.4	219M	config	model
OneFormer	ConvNeXt-L^†	68.5	46.5	83.0	84.0	220M	config	model
OneFormer	DiNAT-L^†	67.6	45.6	83.1	84.0	223M	config	model
OneFormer	ConvNeXt-XL^†	68.4	46.7	83.6	84.6	372M	config	model

COCO

Method	Backbone	PQ	PQ^Th	PQ^St	AP	mIoU	#params	config	Checkpoint
OneFormer	Swin-L^†	57.9	64.4	48.0	49.0	67.4	219M	config	model
OneFormer	DiNAT-L^†	58.0	64.3	48.4	49.2	68.1	223M	config	model

† denotes the backbones were pretrained on ImageNet-22k.

Explore this model on Roboflow

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Deploy OneFormer to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

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OneFormer Annotation Format

OneFormer

uses the

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.
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Convert data between formats

Label data automatically with OneFormer

You can automatically label a dataset using

OneFormer

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

OneFormer

to train a computer vision model.

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