Add BeitForSemanticSegmentation #14096
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sgugger
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Oct 21, 2021
| bias=bias, | ||
| dilation=dilation, | ||
| ) | ||
| self.bn = nn.BatchNorm2d(out_channels) |
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Note that this is the first module I see adding a BatchNorm layer, so if used in the Trainer, we should probably add stuff to handle the weight decay.
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@NielsRogge |
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Hi @kamalkraj, Yes I do plan to add that. However, this will become easier once the Image feature will be available in the Datasets library. |
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Okay @NielsRogge I can update the flax version of beit, after this PR merge |
LysandreJik
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Oct 26, 2021
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* Add first draft * Make forward pass work * Improve conversion script * Add notebook that checks if it works * Add BeitForSemanticSegmentation to the tests * More improvements * Make BeitForSemanticSegmentation consistent with Segformer * Small bug fix * Add BeitForSemanticSegmentation to docs * Make sure model doesn't output hidden states when the user doesn't want to * Make it possible to convert the large model * Fix issue * Fix conversion script for large model * Add auxiliary_head option to semantic segmentation model * Apply suggestions from @sgugger's review * Apply suggestions from code review * Fix failing test Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
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What does this PR do?
This PR is a follow-up of #12994, and adds the semantic segmentation head of BEiT. It's the state-of-the-art model currently for semantic segmentation (i.e. the task of labeling each pixel of an image), on datasets like ADE20k and CityScapes (see this chart on paperswithcode).
Now it's easily available with a HuggingFace API! :)
Models are on the hub: https://huggingface.co/models?search=ade-640
Here's a notebook for quick inference: https://colab.research.google.com/drive/1AS3z0plOhWWibBvDgsQkRbfJB73vSsR3?usp=sharing