| 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: |
| - ImageNet-1k |
| - ImageNet-22k (also called ImageNet-21k as there are 21,843 classes) |
| - COCO detection 2017 |
| - ADE20k (actually, the [MIT Scene Parsing benchmark](http://sceneparsing.csail.mit.edu/), which is a subset of ADE20k) |
| - Cityscapes |
| - VQAv2 |
| - Kinetics-700 |
| - RVL-CDIP |
| - PASCAL VOC |
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| You can read in a label file as follows (using the `huggingface_hub` library): |
|
|
| ``` |
| from huggingface_hub import hf_hub_url, cached_download |
| import json |
| |
| REPO_ID = "datasets/huggingface/label-files" |
| FILENAME = "imagenet-22k-id2label.json" |
| id2label = json.load(open(cached_download(hf_hub_url(REPO_ID, FILENAME)), "r")) |
| id2label = {int(k):v for k,v in id2label.items()} |
| ``` |
|
|
| 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: |
| ``` |
| import json |
| |
| # simple example |
| id2label = {0: 'cat', 1: 'dog'} |
| |
| with open('cats-and-dogs-id2label.json', 'w') as fp: |
| json.dump(id2label, fp) |
| ``` |
| You can then upload it to this repository (assuming you have write access). |
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|
