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Dataset Summary

This is a copy of the full ImageNet dataset consisting of all of the original 21841 clases. It also contains labels in a separate field for the '12k' subset described at at (https://github.com/rwightman/imagenet-12k, https://huggingface.co/datasets/timm/imagenet-12k-wds)

This dataset is from the original fall11 ImageNet release which has been replaced by the winter21 release which removes close to 3000 synsets containing people, a number of these are of an offensive or sensitive nature. There is work in progress to filter a similar dataset from winter21, and there is already ImageNet-21k-P but with different thresholds & preprocessing steps.

Data Splits

Unlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits.

This instance does include a randomly selected validation split consiting of 40 samples for the 11821 classes in ImageNet-12k. The validation split is the exact same as https://huggingface.co/datasets/timm/imagenet-12k-wds and does not fully cover all 22k classes. Beyond the 12k classes (sorted by # samples), the remaining have very few samples per-class. ImageNet-22k is not a balanced dataset.

Train

  • imagenet22k-train-{0000..4095}.tar
  • 13673551 samples over 4095 shards

Validation

  • imagenet22k-validation-{0000..0511}.tar
  • 472840 samples over 512 shards

Processing

I performed some processing while sharding this dataset:

  • All exif tags not related to color space were removed
  • All images with width or height < 48 were removed.
  • All images with the smallest edge > 600 were resized, maintaining aspect so that they were = 600. Improving size & decoding time uniformity for typical pretrain use cases.
  • 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:

  1. Researcher shall use the Database only for non-commercial research and educational purposes.
  2. 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.
  3. 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.
  4. 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.
  5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
  6. 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.
  7. 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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