OneFormer is a state-of-the-art multi-task image segmentation framework that is implemented using transformers.
Here is an overview of the
model:
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.
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.
OneFormer
uses the
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.
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.
Curious about how this model compares to others? Check out our model comparisons.
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