| 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_download | |
| import json | |
| repo_id = "huggingface/label-files" | |
| filename = "imagenet-22k-id2label.json" | |
| id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "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). | |