---
license: other
license_name: imagenet
license_link: https://www.image-net.org/download.php
task_categories:
- image-classification
pretty_name: ImageNet-Winter21
size_categories:
- 10M<n<100M
dataset_info:
  features:
  - name: __key__
    dtype: string
  - name: __url__
    dtype: string
  - name: jpg
    dtype: image
  - name: cls
    dtype: int64
  - name: json
    struct:
    - name: label
      dtype: int64
    - name: class_name
      dtype: string
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: orig_width
      dtype: int64
    - name: orig_height
      dtype: int64
extra_gated_prompt: >-
  By clicking on “Access repository” below, you also agree to ImageNet Terms of
  Access:

  [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the
  ImageNet database (the "Database") at Princeton University and Stanford
  University. In exchange for such permission, 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, Stanford University and Hugging Face 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
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  Stanford University and Hugging Face, 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, Stanford University and Hugging Face 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.
tags:
- webdataset
---
## Dataset Description

- **Homepage:** https://image-net.org/index.php
- **Paper:** https://arxiv.org/abs/1409.0575

### Dataset Summary

This is a copy of the full `Winter21` release of ImageNet in webdataset tar format with JPEG 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

### 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..2047}.tar`
* 13151276 samples over 2048 shards
* 1142.84 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 pre-shuffled across the shards
  
## Additional Information

### Dataset Curators

Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):

- 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.
1. 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.
1. 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.
1. 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.
1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
1. 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.
1. The law of the State of New Jersey shall apply to all disputes under this agreement.

### Citation Information

```bibtex
@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}
}
```