This repository contains the mapping from integer id's to actual label names (in HuggingFace Transformers typically called `id2label`) for several datasets. 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 - Kinetics-400 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).