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ImageNet-Sketch data set for evaluating model's ability in learning (out-of-domain) semantics at ImageNet scale

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ImageNet-Sketch

ImageNet-Sketch

Description

ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes. We construct the data set with Google Image queries "sketch of __", where __ is the standard class name. We only search within the "black and white" color scheme. We initially query 100 images for every class, and then manually clean the pulled images by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then we augment the data set by flipping and rotating the images.

This github repository consists of the scripts we used to conduct query and clean images.

Download the Data

  • Hugging Face dataset
    • one can use the data with
      from datasets import load_dataset
      dataset = load_dataset("imagenet_sketch")  
      
    • Thanks to Nathan Raw for setting up
  • Links
  • Information
    • zip file is 7.8 GB
    • extracted files will be 8.4 GB

ImageNet-Sketch Leaderboard

Method Reference Backbone From Scratch* Top1-Acc. Top5-Acc.
Texture Debiased Augmentation Hermann et al. (NeurIPS 2020) ResNet50 Y 30.9% 51.4%
Anisotropic diffusion Mishra et al. ResNet50 Y 24.49% 41.81%
Random Convolutions Xu et al. AlexNet Y 18.09% 35.40%
RSC Huang et al. (ECCV 2020) AlexNet Y 16.12% 30.78%
PAR Wang et al. (NeurIPS 2019) AlexNet N 13.06% 26.27%
AlexNet Baseline AlexNet N/A 12.04% 24.80%

*This column indicates whether the model is trained from the scratch or built and fine-tuned based a pretrained backbone model.

Analysis

Reference

The data set is introduced together with the following paper in NeurIPS 2019, so if you find this data set helpful, please consider citing it:

Learning Robust Global Representations by Penalizing Local Predictive Power

@inproceedings{wang2019learning,
        title={Learning Robust Global Representations by Penalizing Local Predictive Power},
        author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
        booktitle={Advances in Neural Information Processing Systems},
        pages={10506--10518},
        year={2019}
}

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ImageNet-Sketch data set for evaluating model's ability in learning (out-of-domain) semantics at ImageNet scale

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