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This repository contains the mapping from integer id's to actual label names (in HuggingFace Transformers typically called `id2label`) for several datasets. |
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Current datasets include: |
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- ImageNet-1k |
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- ImageNet-22k (also called ImageNet-21k as there are 21,843 classes) |
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- COCO detection 2017 |
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- ADE20k (actually, the [MIT Scene Parsing benchmark](http://sceneparsing.csail.mit.edu/), which is a subset of ADE20k) |
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- Cityscapes |
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You can read in a label file as follows (using the `huggingface_hub` library): |
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``` |
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from huggingface_hub import hf_hub_url, cached_download |
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import json |
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REPO_ID = "datasets/huggingface/label-files" |
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FILENAME = "imagenet-22k-id2label.json" |
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id2label = json.load(open(cached_download(hf_hub_url(REPO_ID, FILENAME)), "r")) |
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id2label = {int(k):v for k,v in id2label.items()} |
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``` |
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To add an `id2label` mapping for a new dataset, simply define a Python dictionary, and then save that dictionary as a JSON file, like so: |
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``` |
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import json |
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# simple example |
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id2label = {0: 'cat', 1: 'dog'} |
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with open('cats-and-dogs-id2label.json', 'w') as fp: |
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json.dump(id2label, fp) |
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``` |
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You can then upload it to this repository (assuming you have write access). |