File size: 1,219 Bytes
ef3fe9c
 
 
 
43143a0
 
 
 
4463ba2
afa8e37
 
9852417
1ba6c84
7e2773f
ef3fe9c
 
 
 
2f4c0ed
ef3fe9c
 
7e2773f
2f4c0ed
 
ef3fe9c
 
 
cb85862
ef3fe9c
 
 
 
 
 
 
 
cb85862
9852417
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
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).