This is an ONNX version of nvidia/segformer-b3-finetuned-ade-512-512. It was automatically converted and uploaded using this Hugging Face Space.

See the pipeline documentation for image-segmentation: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageSegmentationPipeline

SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.

Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.

SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.

You can use the raw model for semantic segmentation. See the model hub to look for fine-tuned versions on a task that interests you.

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

For more code examples, we refer to the documentation.

The license for this model can be found here.