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, 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).