| | --- |
| | license: apache-2.0 |
| | tags: |
| | - vision |
| | --- |
| | |
| | # ViTMatte model |
| |
|
| | ViTMatte model trained on Composition-1k. It was introduced in the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Yao et al. and first released in [this repository](https://github.com/hustvl/ViTMatte). |
| |
|
| | Disclaimer: The team releasing ViTMatte did not write a model card for this model so this model card has been written by the Hugging Face team. |
| |
|
| | ## Model description |
| |
|
| | ViTMatte is a simple approach to image matting, the task of accurately estimating the foreground object in an image. The model consists of a Vision Transformer (ViT) with a lightweight head on top. |
| |
|
| | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitmatte_architecture.png" |
| | alt="drawing" width="600"/> |
| |
|
| | <small> ViTMatte high-level overview. Taken from the <a href="https://arxiv.org/abs/2305.15272">original paper.</a> </small> |
| |
|
| | ## Intended uses & limitations |
| |
|
| | You can use the raw model for image matting. See the [model hub](https://huggingface.co/models?search=vitmatte) to look for other |
| | fine-tuned versions that may interest you. |
| |
|
| | ### How to use |
| |
|
| | We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/vitmatte#transformers.VitMatteForImageMatting.forward.example). |
| |
|
| | ### BibTeX entry and citation info |
| |
|
| | ```bibtex |
| | @misc{yao2023vitmatte, |
| | title={ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers}, |
| | author={Jingfeng Yao and Xinggang Wang and Shusheng Yang and Baoyuan Wang}, |
| | year={2023}, |
| | eprint={2305.15272}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CV} |
| | } |
| | ``` |
