[Proposal] Image segmentation pipeline
# What does this PR do?
- Currently very low-level results, simply classification masks + score
+ label for each detected class
- Could support panoptic, instance segmentation, bounding boxes and even
part of instance segmentation (might require adding the "parent" info in
addition but that's about it.)
- Happy to hear some thoughts about the design.
- Future could maybe add a "aggregation_strategy" like, which could maybe
output a single image with color filters on top of the classes and so on
(or leave this reduction outside the pipeline). Getting final images like
https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5#scrollTo=8IRGo8d0qkgR 
is still a bit involved for users that simply want to "see" outputs.



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@LysandreJik @NielsRogge

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@NielsRogge can you give this pipeline a look when you get a chance?<|||||>Thanks for this proposal! So the output of the API would be `[{mask, score, label}, ...]`, right? And the shape of `mask` is (height, width)?

I wonder whether this can support any image segmentation model. I'm currently working on implementing [SegFormer](https://arxiv.org/abs/2105.15203) (a new model by NVIDIA), which is a semantic segmentation model. It takes an image as input and produces a segmentation map as output (i.e. it assigns a class to each pixel). This model will only have `outputs.logits`, and they are of shape (batch_size, num_labels, height/4, width/4) - the logits are produced at 1/4th of the original image size. The SegFormer model does not have `outputs.pred_masks`, for example. 

This pipeline should support all forms of image segmentation, right? Panoptic, semantic, instance, etc? I think that if we want that, then we should first define what models for each of these forms of segmentation should produce in their `outputs`.<|||||>@NielsRogge 

I think pipelines should support as much different models as possible, regardless of the model output. (and clean error when model is not supported)
Proposed implem uses `pred_masks` because that's what currently available for DetR but if some other arch have different outputs, it should be the pipeline's role to be able to use `.logits` instead and still produce the same outputs.

Those are different kind of models, right ? (Like XXXXForSegmentation vs XXXXForPanopticSegmentation)
If yes, then I think it's kind of routine to have switches on those for the pipelines. Less desirable would be to switch on actual model outputs (.logits vs .pred_masks) , and something that we should really strive to avoid is actually looking at the model arch to decide.

Regardless, I think we can recover masks from raw logits, right ? If yes, then I think that proves that current output is good as it would enable pipeline to support SegFormer too.

Would you agree? <|||||>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/master/CONTRIBUTING.md) are likely to be ignored.<|||||>Unstale<|||||>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/master/CONTRIBUTING.md) are likely to be ignored.