This repository contains PyTorch code for the IIC paper.
IIC is an unsupervised clustering objective that trains neural networks into image classifiers and segmenters without labels, with state-of-the-art semantic accuracy.
We set 9 new state-of-the-art records on unsupervised STL10 (unsupervised variant of ImageNet), CIFAR10, CIFAR20, MNIST, COCO-Stuff-3, COCO-Stuff, Potsdam-3, Potsdam, and supervised/semisupervised STL. For example:
Commands used to train the models in the paper here. There you can also find the flag to turn on prediction drawing for MNIST:
How to download all our trained models here.
How to set up the segmentation datasets here.