Difference between facebook/dinov2-base and timm/vit_base_patch14_dinov2.lvd142m
Author: lombardata
Pull request: no

### Feature request

Hi all,
Maybe this question will be very stupid but I do not understand the difference between these two models :
facebook/dinov2-base and timm/vit_base_patch14_dinov2.lvd142m (obviously this last model without the linear classifier on top).
In my understanding, both are implementations of the DinoV2 base model pretrained on the LVD-142M dataset with self-supervised DINOv2 method. The only thing that changes is the library behind?
If I want to finetune an image classification Neural network with the DinoV2 backbone, both will give me the same results?
Thank you in advance for the explanation !

### Motivation

Choose the right backbone before training image classification models on top of DinoV2 backbones

### Your contribution

clarify similarity between Dinov2 models

Comments:
@NielsRogge do you have any suggestion?
Thanks in advance !

Hi @lombardata,

It's a valid question - thanks for asking. So Timm is a more research-focused library providing access to state-of-the-art image backbones, including recent ones like DINOv2 and SigLIP. The library provides a fully-fledged [train.py](https://github.com/huggingface/pytorch-image-models/blob/main/train.py) script which allows you to reproduce numbers of the original numbers up to a great extent. The library is currently aimed at image classification.

The Transformers library on the other hand is more production-focused, aimed at machine learning engineers that would like to put models in production rather than doing cutting edge research on them. It also aims to support various downstream tasks besides image classification.

Usually, the strategy is to port models from Timm to Transformers if we think they are worthy additions, such that they can then also benefit from tools like the [Optimum](https://huggingface.co/docs/optimum/index) library, which provides ONNX exports of these models, among other optimizations. Another reason is that we usually also add various decoding heads to those models, e.g. [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) in the Transformers library, which is a depth estimation framework, [can now be used with DINOv2 as backbone](https://huggingface.co/models?pipeline_tag=depth-estimation&other=dinov2&sort=trending). The same goes for [DETR](https://huggingface.co/docs/transformers/model_doc/detr) for instance, which leverages ResNet, Swin Transformer as backbones.

Thank you very much for your answer.

> Usually, the strategy is to port models from Timm to Transformers if we think they are worthy additions, 

So in terms of performance, if I use a Timm model or the equivalent Transformer model I'm supposed to get the same results?

@lombardata It depends exactly what you mean by performance. 

In terms of running time, probably not. Each library has it's own way of structuring the models which means one is likely faster than the other. In terms of inference from a pretrained checkpoint, yes, we'd expect them to be very close. I wouldn't be surprised if there were some small differences however because all sorts of tricky things can influence values. In terms of fine-tuning, I would expect some differences. Timm has a greater emphasis on replication of training results and so it more likely to match the original model in this case. 

Thank you very much !