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Dataset Summary

This is a copy of the full Winter21 release of ImageNet in webdataset tar format with WEBP encoded images. This release consists of 19167 classes, 2674 fewer classes than the original 21841 class Fall11 release of the full ImageNet.

The classes were removed due to these concerns: https://www.image-net.org/update-sep-17-2019.php

This is the same contents as https://huggingface.co/datasets/timm/imagenet-w21-wds but encoded in webp at ~56% of the size, shard count halved.

Data Splits

The full ImageNet dataset has no defined splits. This release follows that and leaves everything in the train split. Shards are shuffled so validation & test splits can be made by dividing at the shard level.

Train

imagenet12k-train-{0000..1023}.tar
13151276 samples over 1024 shards
645.65 GB

Processing

I performed some processing while sharding this dataset:

All exif tags not related to color space were removed
A set of 20 partially corrupted images in the original tar file were corrected and re-encoded
All images with width or height < 32 were removed, ~2000 images.
All images with the smallest edge > 768 were resized, maintaining aspect so that they were = 768. Improving size & decoding time uniformity for typical pretrain use cases.
Images were re-encoded in WEBP
Images were pre-shuffled across the shards

Additional Information

Dataset Curators

Authors of [1] and [2]:

Olga Russakovsky
Jia Deng
Hao Su
Jonathan Krause
Sanjeev Satheesh
Wei Dong
Richard Socher
Li-Jia Li
Kai Li
Sean Ma
Zhiheng Huang
Andrej Karpathy
Aditya Khosla
Michael Bernstein
Alexander C Berg
Li Fei-Fei

Licensing Information

In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:

Researcher shall use the Database only for non-commercial research and educational purposes.
Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
The law of the State of New Jersey shall apply to all disputes under this agreement.

Citation Information

@article{imagenet15russakovsky,
    Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
    Title = { {ImageNet Large Scale Visual Recognition Challenge} },
    Year = {2015},
    journal   = {International Journal of Computer Vision (IJCV)},
    doi = {10.1007/s11263-015-0816-y},
    volume={115},
    number={3},
    pages={211-252}
}

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