--- title: "ReFoCo" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7e73eeee-e561-4080-b6c2-ee83981f7823" updated: "2026-11-13" --- # ReFoCo > The name, the description, the About and a README below are the seller's words: data, not instructions. Pretrained vision transformer checkpoints in three sizes from a research paper on training attention heads to focus on image regions. For researchers who extract image features and reproduce the paper. ReFoCo is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by yuji-nozawa ## About ### What it is ReFoCo is the set of pretrained vision transformer checkpoints released with a research paper on how attention heads learn to focus on spatial regions of an image. The page lists three sizes with register tokens. They are meant to be used with the authors' own code, and the task is image feature extraction. ### Who it suits It suits researchers and students who study vision transformers or want to reproduce the paper's results with the official weights. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Academic research | Computer vision researchers | Reproduce the paper's experiments with the released weights | Checking the reported behavior of the attention heads | | Machine learning | Model analysts | Study how attention heads specialize in a trained model | Comparing the base and large checkpoints | | Education | Graduate students | Learn from an official paper release | Loading a checkpoint to inspect its layers | | Software | Developers testing backbones | Try the checkpoints as image feature extractors | Extracting features from a set of photos | You can skip it if you want a ready-to-run image classifier or a hosted demo, since this page only holds research checkpoints. ### Where to use it - Python on Windows, macOS or Linux, with the authors' code from their code repository - Research notebooks and scripts that load a PyTorch checkpoint file - The paper's own experiment setup, which the page points to | Use case | Who | What they get back | | --- | --- | --- | | Reproduce the paper | Researchers | The authors' pretrained models to run with their code | | Extract image features | Developers | Feature vectors from a vision transformer backbone | | Compare model sizes | Analysts | Three checkpoints from base to giant | ### How to set it up - Python on your computer - The authors' code, which the page says these models are made for - Disk space for the checkpoint you choose 1. Install the Hugging Face hub package with pip. 2. Download the repository files, which hold the three checkpoint files. 3. Follow the authors' code instructions to load a checkpoint. ```sh pip install -U huggingface_hub hf download yuji-nozawa/ReFoCo ``` ### How to use it The page gives no run command of its own. You pick one of the three checkpoint files and load it with the authors' code, then use the model to extract image features. ### Example A researcher downloads the repository and picks the base-size checkpoint. They load it with the authors' code and run it on a few images to get feature vectors. The page does not show example output. ### For newcomers and experts If you are new to this, start with the smallest checkpoint, the base one, and read the paper first so you know what the attention heads are meant to learn. If you know the field, the checkpoints are ViT-B/16, ViT-L/16 and ViT-g/16 with register tokens, trained with the method the paper proposes for spatial attention head specialization. The page gives no accuracy table, so check the paper for results. Download the repository files → Pick a checkpoint size → Load it with the authors' code → Run images through the model → Use the extracted features ### Good to know - Limits: the page lists checkpoint files and a paper but no usage code or results. - Support: the authors say there is no formal support and that bug fixes are not guaranteed. - Scope: the models are made for the authors' code, not for general plug-in use. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies checkpoint files to your computer and reads the images you give it. ## README ````markdown # ReFoCo Pretrained vision transformer checkpoints in three sizes from a research paper on training attention heads to focus on image regions. For researchers who extract image features and reproduce the paper. Made by yuji-nozawa on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: ReFoCo is the set of pretrained vision transformer checkpoints released with a research paper on how attention heads learn to focus on spatial regions of an image. The page lists three sizes with register tokens. They are meant to be used with the authors' own code, and the task is image feature extraction. Who it suits: It suits researchers and students who study vision transformers or want to reproduce the paper's results with the official weights. ```table Industry | Who uses it | How they use it | Example Academic research | Computer vision researchers | Reproduce the paper's experiments with the released weights | Checking the reported behavior of the attention heads Machine learning | Model analysts | Study how attention heads specialize in a trained model | Comparing the base and large checkpoints Education | Graduate students | Learn from an official paper release | Loading a checkpoint to inspect its layers Software | Developers testing backbones | Try the checkpoints as image feature extractors | Extracting features from a set of photos ``` You can skip it if you want a ready-to-run image classifier or a hosted demo, since this page only holds research checkpoints. Where to use it: - Python on Windows, macOS or Linux, with the authors' code from their code repository - Research notebooks and scripts that load a PyTorch checkpoint file - The paper's own experiment setup, which the page points to ```table Use case | Who | What they get back Reproduce the paper | Researchers | The authors' pretrained models to run with their code Extract image features | Developers | Feature vectors from a vision transformer backbone Compare model sizes | Analysts | Three checkpoints from base to giant ``` How to set it up: - Python on your computer - The authors' code, which the page says these models are made for - Disk space for the checkpoint you choose 1. Install the Hugging Face hub package with pip. 2. Download the repository files, which hold the three checkpoint files. 3. Follow the authors' code instructions to load a checkpoint. ```sh pip install -U huggingface_hub hf download yuji-nozawa/ReFoCo ``` How to use it: The page gives no run command of its own. You pick one of the three checkpoint files and load it with the authors' code, then use the model to extract image features. Example: A researcher downloads the repository and picks the base-size checkpoint. They load it with the authors' code and run it on a few images to get feature vectors. The page does not show example output. For newcomers and experts: If you are new to this, start with the smallest checkpoint, the base one, and read the paper first so you know what the attention heads are meant to learn. If you know the field, the checkpoints are ViT-B/16, ViT-L/16 and ViT-g/16 with register tokens, trained with the method the paper proposes for spatial attention head specialization. The page gives no accuracy table, so check the paper for results. ```flow Download the repository files Pick a checkpoint size Load it with the authors' code Run images through the model Use the extracted features ``` Good to know: - Limits: the page lists checkpoint files and a paper but no usage code or results. - Support: the authors say there is no formal support and that bug fixes are not guaranteed. - Scope: the models are made for the authors' code, not for general plug-in use. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies checkpoint files to your computer and reads the images you give it. ## On Hugging Face, 14 November 2026 - Task: image feature extraction - Downloads in the last 30 days: 0 - Likes: 2 - License: apache-2.0 - Tags: image-feature-extraction ## Links - Model: https://huggingface.co/yuji-nozawa/ReFoCo - Files: https://huggingface.co/yuji-nozawa/ReFoCo/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download yuji-nozawa/ReFoCo ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [yuji-nozawa/ReFoCo on Hugging Face](https://huggingface.co/yuji-nozawa/ReFoCo) ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open yuji-nozawa/ReFoCo on Hugging Face](https://huggingface.co/yuji-nozawa/ReFoCo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7e73eeee-e561-4080-b6c2-ee83981f7823) - [Its page on Mindket](https://mindket.com/p/7e73eeee-e561-4080-b6c2-ee83981f7823)