Add D-FINE to KerasHub

[harshaljanjani]

Description of the change

Welcome D-FINE to the KerasHub family of models!
D-FINE, a powerful real-time object detector, sets a new state-of-the-art benchmark for object detection on KerasHub. It achieves outstanding localization precision by redefining the bounding box regression task in DETR models. Additionally, it incorporates lightweight optimizations in computationally intensive modules and operations, striking a better balance between speed and accuracy. Specifically, D-FINE-L/X achieves 54.0%/55.8% AP on the COCO dataset at 124/78 FPS on an NVIDIA T4 GPU. When pretrained on Objects365, D-FINE-L/X attains 57.1%/59.3% AP, surpassing all existing real-time detectors.

Closes the second half and thus, the complete issue #2271

Results in Action of KerasHub's D-FINE

Model Predictions	Numerics Matching (Visit the Colab notebook for the complete results)

Colab Notebook

D-FINE: Complete Workflow with Predictions and Numerics Matching

Checklist

• I have added all the necessary unit tests for my change.
• I have verified that my change does not break existing code and works with all backends (TensorFlow, JAX, and PyTorch).
• My PR is based on the latest changes of the main branch (if unsure, rebase the code).
• I have followed the Keras Hub Model contribution guidelines in making these changes.
• I have followed the Keras Hub API design guidelines in making these changes.
• I have signed the Contributor License Agreement.

[harshaljanjani]

The tests fail due to pending reviews on the HGNetV2 dependency. Once it is merged, the D-FINE PR will be open for review, and the tests will pass, as demonstrated in the notebook.

[divyashreepathihalli]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces the D-FINE model to KerasHub, including its architecture, layers, tests, and a checkpoint conversion script. The implementation is comprehensive and well-structured. I've provided a few suggestions to improve code clarity, maintainability, and correctness. Overall, this is a solid contribution.

[harshaljanjani]

@divyashreepathihalli @mattdangerw D-FINE is ready for its first round of reviews!
As discussed, we're leaving out the task model from the scope of this PR, given the sheer volume of the code, since the task model is a 1000+ LOC effort in itself. I've not only covered the numerics check, but also the examples we'd add to the quickstart notebook on Kaggle once merged, in the Colab notebook linked in the PR description!

[mattdangerw]

Thanks! Nice work. Just some initial comments.

In general, now that this is up and working let's see if we can find anywhere to cut complexity if we can. Anything we can do to same lines of code (without playing code golf) will probably help keep this maintainable for the future.

[harshaljanjani]

Thanks for the reviews @mattdangerw. Yeah let's definitely cut down the complexity wherever possible for maintainability, I'll look into it!

[harshaljanjani]

@mattdangerw Could you please check if all your comments have been addressed when you have the time, thanks a lot!

[harshaljanjani]

@mattdangerw @divyashreepathihalli
Good day, just a gentle reminder for a timely review of this PR, thank you!

[sachinprasadhs]

Thanks for the PR!
I have added few comments, mainly focusing on our standard design process.

[harshaljanjani]

@sachinprasadhs
Thanks for taking the time to review. I've addressed the concerns to the best of my ability.

[sachinprasadhs]

Thanks for addressing all the comments, this looks better now. Just one place you might have missed to make change, added comment.

[harshaljanjani]

@sachinprasadhs Resolved, thanks!

[divyashreepathihalli]

Thank you for the PR Harshal! left a few comments1

[harshaljanjani]

Good day @divyashreepathihalli!
Thanks for taking the time to review, I've left responses in a few comments and made changes to resolve them to the best of my ability!

[sachinprasadhs]

Pending changes #2318 (comment) as discussed with team.
cc: @divyashreepathihalli