Dataset Card for ImageNet

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://image-net.org/index.php
• Repository:
• Paper: https://arxiv.org/abs/1409.0575
• Leaderboard: https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171
• Point of Contact: mailto: imagenet.help.desk@gmail.com

Dataset Summary

Supported Tasks and Leaderboards

• image-classification: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available here.

670 778 794 387 650
217 691 564 909 364
737 369 430 531 124
755 930 755 512 152

Languages

Dataset Structure

Data Instances

{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
  'label': 23
}

Data Fields

• image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
• label: an int classification label. -1 for test set as the labels are missing.

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
2. Collecting the candidate image for these object categories using a search engine.
3. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

1. A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
2. A study has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
3. Another study more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
4. ImageNet data with face obfuscation is also provided at this link
5. A study on genealogy of ImageNet is can be found at this link about the "norms, values, and assumptions" in ImageNet.
6. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet.

Other Known Limitations

1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [1] [2] [3].

Additional Information

Dataset Curators

• 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

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

Contributions