Support dinov2 models (small, base, large, giant) from VisionTransformer backbone

[wonjuleee]

Summary

test/acc	torch.hub	timm
medium	0.839562	0.827991
large	0.909567	0.8922

With the TIMM implementation, we can enable deterministic and provide more functionalities (LoRA, ToMe, etc.)

How to test

Checklist

• I have added unit tests to cover my changes.​
• I have added integration tests to cover my changes.​
• I have ran e2e tests and there is no issues.
• I have added the description of my changes into CHANGELOG in my target branch (e.g., CHANGELOG in develop).​
• I have updated the documentation in my target branch accordingly (e.g., documentation in develop).
• I have linked related issues.

License

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  Feel free to contact the maintainers if that's a concern.
• I have updated the license header for each file (see an example below).

# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

[kprokofi]

Can we now remove dino_v2.DINOv2RegisterClassifier? Could you update also semantic segmentation models? Or do you plan to enable it in different PR?
Also, could you please provide experiments result and comparison with existing DINOV2?

[wonjuleee]

Can we now remove dino_v2.DINOv2RegisterClassifier? Could you update also semantic segmentation models? Or do you plan to enable it in different PR? Also, could you please provide experiments result and comparison with existing DINOV2?

Thank you for the good question. I updated the PR summary with experiment results.
For the deletion of dinov2.py and support segmentation dinov2, I will come with next PR :)