sketch
Datasets
All datasets matching “sketch”wds_imagenet_sketchimagenet-sketch-dataimagenet_sketchImageNet-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.wds_imagenet_sketch_test
ImageNet-Sketch (Test set only)
Original paper: Learning Robust Global Representations by Penalizing Local Predictive Power
Homepage: https://github.com/HaohanWang/ImageNet-Sketch
Bibtex:
@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}… See the full description on the dataset page: https://huggingface.co/datasets/djghosh/wds_imagenet_sketch_test.trellis500k-sketchfab-archiveswhest-p2-bakev2-d8b-sketch-g00
whest-p2-bakev2 — cumulant sketches of 16×1024 ReLU MLPs (round 1, 2026-09-13)
Monte-Carlo cumulants of the pre-/post-activations of 1024-wide, 16-layer ReLU MLPs under
standard-normal inputs, stored as Ω-sketches (every net) plus a few dense n×n blocks
(validation nets). Companion code: ap_p2_bakev2_schema.py (seeds, Ω generator,
conversions, loader), ap_p2_bakev2.py (bake), ap_p2_bakev2_check.py (validation).
Schema version bakev2-r1-2026-09-13.
bake
family / nets
tier… See the full description on the dataset page: https://huggingface.co/datasets/keenanpepper/whest-p2-bakev2-d8b-sketch-g00.