SegFormer is a computer vision framework used in semantic segmentation tasks, implemented with transformers.
Here is an overview of the
model:
With ViT as a backbone showing great promise, various papers began to build on the idea and innovate to address issues of low resolution and high computational cost. And, while performance continued to improve with each new method, these papers seemed to focus solely on the design of the transformer encoder and neglected the decoder. Enter SegFormer. SegFormer sets itself apart with:
The novel encoder is able operate at arbitrary resolutions without impacting performance. Additionally, the encoder is able to generate both high resolution and low resolution features in contrast to ViT. The decoder design is able to combine both local and global attention to produce high quality representations at low cost.
With these novel improvements, SegFormer sets a new SOTA on ADE20K, Cityscapes, and COCO-Stuff semantic segmentation datasets.
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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.
SegFormer
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
SegFormer
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
SegFormer
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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