Fixing class embedding selection in owl-vit
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Fixes # For OWL-ViT image-guided object detection, there is a mistake in selecting the best embedding (the most distinct one with high IoU). Specifically;
selected_inds is a [num_inds, 1] dimensional tensor, where the indexes indicate which queries had a high IoU with target object bbox. But, as selected_inds[0] was selected, only the first of all the possible queries is selected. Specifically, instead of selected_embeddings being a [num_inds, D] dimensional tensor, it is a [1, D] dimensional tensor. This led ultimately to the first query always being selected, not the most unique one as required. 

An error is not raised. To see this is the case, just add a print statement of 'torch.argmin(mean_sim)' here:
https://github.com/huggingface/transformers/blob/01734dba842c29408c96caa5c345c9e415c7569b/src/transformers/models/owlvit/modeling_owlvit.py#L1505

& you will see it is always 0. 


- `transformers` version: 4.28.1
- Platform: Linux-5.4.0-109-generic-x86_64-with-glibc2.31
- Python version: 3.11.3
- Huggingface_hub version: 0.14.1
- Safetensors version: not installed
- PyTorch version (GPU?): 2.0.0+cu117 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- 
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@sgugger
@NielsRogge
@alaradirik 
@amyeroberts
_The documentation is not available anymore as the PR was closed or merged._<|||||>@orrzohar thank you for opening the PR! I'll take double check the original code and the forward pass shortly.<|||||>After this fix I have problem with predictions.

I Use [Colab demo](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb) for owl-vit
And on example photo with cats I have only one prediction. Looks suspicious and not the same as original repo

<img width="945" alt="Screenshot 2023-05-09 at 16 13 13" src="https://github.com/huggingface/transformers/assets/55710648/02503c77-acb1-4d97-95a2-3d2f0ff6599f">

@alaradirik 
<|||||>> Hi @MaslikovEgor, Hub demos are deployed once and not updated unless triggered. I'm rebooting the demo to reflect the changes.

Nice to meet you!

Yeah, I understand this. But in google colab demo we install fresh transformers from source:

`!pip install git+https://github.com/huggingface/transformers.git`

So this is the problem with this changes

@alaradirik <|||||>I found when evaluating COCO that mAP@0.5 increases from 6 to 37. This is still below the expected 44+, but closer to the reported/expected performance.

I am still trying to figure out why.