Object detection experiment for transformers
Author: SangbumChoi
Pull request: yes

# What does this PR do?

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@qubvel As we discussed, I will generate the standard metric for 

**how well the transformers object detection model finetune in diverse domain of data?**

In this step I will focus on 

batch_size | learning rate | model | dataset | AP | training time | required memory
-- | -- | -- | -- | -- | -- | --

on different domain of dataset.
https://www.notion.so/sangbumchoi/HuggingFace-OD-train-1afb8ded2e1680daa2dcfdc3e11aa15d?pvs=4

For the part that changed is

In this step D-Fine was not perfectly finished for the finetune feature. You can see the https://github.com/Peterande/D-FINE/blob/4a1f73a8bcfac736a88abde9596d87f116d780a7/src/zoo/dfine/dfine_criterion.py#L357. This is `pre_output` related auxiliary layer which is related to first layer to use `pre_bbox_head` .https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/d_fine/modeling_d_fine.py#L1195

This is initial experiment of four models that you have suggested.

![Screenshot 2025-05-05 at 2 54 56 PM](https://github.com/user-attachments/assets/65c59088-45ae-4af2-85a6-1a2f2c3694f8)



## 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).
- [ ] Did you write any new necessary tests?


## 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.

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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

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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.

cc @VladOS95-cyber

For the PR I will break into two parts :)