Add model doc for ViTPose with quantization and attention visualization
Author: saumanraaj
Pull request: yes

# What does this PR do?
This PR adds a new model documentation file for ViTPose, following the standardized doc format seen in models like BERT
<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->




## Before submitting
- [✓ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ✓] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [✓ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ✓] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).



## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Comments:
Hi 👋, thank you for opening this pull request! The pull request is converted to draft by default. The CI will be paused while the PR is in draft mode. When it is ready for review, please click the `Ready for review` button (at the bottom of the PR page). This will assign reviewers and trigger CI.

Hi @stevhliu @qubvel! 👋

This PR adds standardized documentation for the ViTPose model, including quantization and attention visualization examples, consistent with other model cards like BERT.

Would you mind reviewing when you get a chance? Thank you so much!


@stevhliu, this PR only modifies model docs for ViTPose. The CI failed due to a `kenlm` build issue unrelated to my changes. Can you please take a look or help rerun it?


The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_37089). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Final Updates Applied – Ready for Review
Hi @stevhliu, @qubvel  thank you for your thoughtful and detailed feedback. I’ve reviewed and addressed all the requested changes. Below is a summary of the updates made:

Cleanup of Deprecated or Invalid Examples
Removed the unsupported pipeline(...) usage for pose estimation.

Removed the transformers-cli example, which does not support image inputs.

Removed the AttentionMaskVisualizer block, as it is not compatible with ViTPose.

Removed the outdated usage snippet involving AutoProcessor and VitPoseForPoseEstimation without bounding box preprocessing.

Updated Code and Documentation
Rewrote the main usage section with a complete two-stage pipeline using RT-DETR for person detection and ViTPose for keypoint estimation.

Added support for dataset_index in ViTPose++ models, with linked datasets and an example showing how to pass the appropriate index.

Updated all model loading to use AutoModelForObjectDetection and AutoModelForPoseEstimation for consistency with Transformers conventions.

Replaced the Intel-specific quantization backend (optimum.intel) with torchao, and included a clear example demonstrating int4 quantization.

Added keypoint visualization examples using both the supervision library and OpenCV with customizable color palettes and connections.

Revised the Resources section to include links to the Hugging Face Spaces demo, a detailed notebook, and the original GitHub codebase.

Clarified and expanded the Notes section to highlight the top-down architecture, preprocessing with AutoProcessor, and relevant datasets.



Nothing have been changed btw

Hi @stevhliu  @qubvel ,

I've applied all the suggested changes and finalized the ViTPose model card. The updates include:

Cleaned up outdated usage examples and unsupported pipelines

Added complete working usage with AutoProcessor and AutoModelForPoseEstimation

Included a quantization section using torchao

Added keypoint visualization examples using both supervision and OpenCV

Clarified dataset_index usage for ViTPose++ with linked datasets

Updated notes, citations, and formatting for clarity and consistency

Please let me know if there's anything else I should address. Thanks again for the detailed feedback!

Best,
Sauman