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imagenet-r

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metadata
license: mit
dataset_info:
  config_name: test
  features:
    - name: image
      dtype: image
    - name: wnid
      dtype: string
    - name: class_name
      dtype: string
  splits:
    - name: test
      num_bytes: 2355062808
      num_examples: 30000
  download_size: 2148902579
  dataset_size: 2355062808
configs:
  - config_name: test
    data_files:
      - split: test
        path: test/test-*
    default: true

ImageNet-R

This repo is made to facilitate the evaluation of various pretraining models. It's constructed from the source file provided by official implementation.

Usage 

from datasets import load_dataset

dataset = load_dataset('axiong/imagenet-r')

Dataset Summary

ImageNet-R(endition) contains art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game renditions of ImageNet classes.

ImageNet-R has renditions of 200 ImageNet classes resulting in 30,000 images.

ImageNet-R is a dataset proposed on ICCV 2021 by Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt and Justin Gilme. The detailed introduction could be found in their paper 'The Many` Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization'.

image/jpg

Example Data

  • wnid is the ID from wordnet, used to indicate the class label.
  • class_name is the the corresponding class name of wnid
[
  {
    "image": <PIL Image>,
    "wnid": "n02088094",
    "class_name": "afghan_hound"
  },
  {
    "image": <PIL Image>,
    "wnid": "n07697537",
    "class_name": "hotdog"
  }
]

Citation 

@article{hendrycks2021many,
  title={The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization},
  author={Dan Hendrycks and Steven Basart and Norman Mu and Saurav Kadavath and Frank Wang and Evan Dorundo and Rahul Desai and Tyler Zhu and Samyak Parajuli and Mike Guo and Dawn Song and Jacob Steinhardt and Justin Gilmer},
  journal={ICCV},
  year={2021}
}

About Me

I am Weixiong Lin from SJTU, my research interests include multimodal representation learning, foundation model, data acceleration, etc. Feel free to contact me if you are seeking cooperations.