Awesome

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
- from Google Drive
- from Kaggle
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

An interesting analysis is performed at Kaggle, evaluating a large number of CNNs with our data set
- Can pretrained ImageNet models generalize to sketches
A comprehensive evaluation of popular vision models on ImageNet-Sketch
- Pytorch Image Models' performances on ImageNet Sketch (300+ models)

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}
}