Pipeline support for image similarity 
### Feature request

Given that we have a [tutorial notebook on image similarity](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_similarity.ipynb) and an [upcoming blog post](https://github.com/huggingface/blog/pull/663), and given the usefulness of the use case, it's time we added a pipeline for this task.

### Motivation

Image similarity is an important use case in the industry. 



### Your contribution

Happy to contribute the pipeline. 

Following describes some of the design decisions I had in mind for this pipeline. 

By default, we provide the [most downloaded image classification model](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads) (trained on ImageNet-1k). 

Image inputs to the `__call__()` of the pipeline would be similar to an [`ImageClassificationPipeline`](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.ImageClassificationPipeline) except the the input needs to be a list of two images / URLs, etc. 

We return a matrix quantifying the similarity scores (cosine similarity) between all the input images. 

We might want to also provide recommendations to the users when using this pipeline. For example, the input images would need to be provided in accordance with the provided model. If you're using a model that was pre-trained / fine-tuned on medical images then there's no point in passing images of cats and dogs to compute similarity over. 

Related: https://github.com/huggingface/hub-docs/issues/572
Ccing @NielsRogge @osanseviero @nateraw <|||||>We don't currently have a `text-similarity`/`sentence-similarity` pipeline either, right? I think for that task, folks use the `feature-extraction` pipeline to get embeddings, then just compute the similarity. [Here's an example.](https://huggingface.co/optimum/sbert-all-MiniLM-L6-with-pooler)

So, with that in mind, maybe the pipeline could be an equivalent `image-feature-extraction` for vision?

Unfortunately, the name '<modality>-feature extractor' is quite confusing since that's what the image processing utils are called still (I think?).<|||||>> So, with that in mind, maybe the pipeline could be an equivalent `image-feature-extraction` for vision?

Yes, let's do that!

> Unfortunately, the name '-feature extractor' is quite confusing since that's what the image processing utils are called still (I think?).

@amyeroberts worked on porting the image feature extractors to `***ImageProcessor` ([example](https://huggingface.co/docs/transformers/model_doc/vit#transformers.ViTImageProcessor)). We also throw a warning when users cal `XXXFeatureExtractor` from the library. 

With that in mind, `image-feature-extraction` does seem alright to me. 

<|||||>Even with it being legacy, I'm slightly concerned this may become confusing to some users. I'll let some others weight in here!

I can live with `image-feature-extraction` if nobody else vetos <|||||>Afaik `feature-extraction` already works for image feature extraction as well (see https://huggingface.co/google/vit-base-patch16-224-in21k for example)<|||||>> Afaik `feature-extraction` already works for image feature extraction as well (see https://huggingface.co/google/vit-base-patch16-224-in21k for example)

I need to try it out to verify if it works with the feature extraction pipeline. Will confirm soon. <|||||>Yes I'm not sure there's a need for a new `image-feature-extraction` pipeline, one can leverage the `feature-extraction` pipeline already<|||||>I verified the feature-extraction pipeline, and it seems like it always assumes the preprocessing will use a tokenizer as opposed to an image processor:

```py
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-10-55a624159a32> in <module>
      1 image_one = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"
      2 
----> 3 image_feature_extractor(image_one)

3 frames
/usr/local/lib/python3.8/dist-packages/transformers/pipelines/feature_extraction.py in __call__(self, *args, **kwargs)
    103             A nested list of `float`: The features computed by the model.
    104         """
--> 105         return super().__call__(*args, **kwargs)

/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py in __call__(self, inputs, num_workers, batch_size, *args, **kwargs)
   1072             return self.iterate(inputs, preprocess_params, forward_params, postprocess_params)
   1073         else:
-> 1074             return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
   1075 
   1076     def run_multi(self, inputs, preprocess_params, forward_params, postprocess_params):

/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py in run_single(self, inputs, preprocess_params, forward_params, postprocess_params)
   1078 
   1079     def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
-> 1080         model_inputs = self.preprocess(inputs, **preprocess_params)
   1081         model_outputs = self.forward(model_inputs, **forward_params)
   1082         outputs = self.postprocess(model_outputs, **postprocess_params)

/usr/local/lib/python3.8/dist-packages/transformers/pipelines/feature_extraction.py in preprocess(self, inputs, **tokenize_kwargs)
     77     def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]:
     78         return_tensors = self.framework
---> 79         model_inputs = self.tokenizer(inputs, return_tensors=return_tensors, **tokenize_kwargs)
     80         return model_inputs
     81 

TypeError: 'NoneType' object is not callable
```

The design choice seems reasonable in case image feature extraction was not considered. 

[Here's](https://colab.research.google.com/gist/sayakpaul/4782955c210397dfcc5306f028cbad3d/feature_extraction_image_similarity.ipynb) my Colab Notebook. 

@nateraw @NielsRogge @osanseviero <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.<|||||>Closing this as we internally deprioritized this pipeline. 