metadata
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: class
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 3879773546902
num_examples: 13153480
download_size: 3876745904054
dataset_size: 3879773546902
NOTE: I have recaptioned all images here
This dataset is the entire 21K ImageNet dataset with about 13 million examples and about 19 thousand classes as strings (for some reason it only had ~19K classes instead of 21K).
The images are in PNG format. They can be decoded like in the following example
import io
from PIL import Image
Image.open(io.BytesIO(row["image"]))
where row["image"] are the raw image bytes.