hehefan / DPSIS

Unsupervised Visual Representation Learning via Dual-level Progressive Similar Instance Selection (DPSIS)

Setup

All our code is implemented in PyTorch. Installation instructions are as follows:

pip install torch 
pip install torchvision

Data

data/cifar10: https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz

data/cifar100: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz

Note that cifar10 and cifar100 can be automatically downloaded and extracted when first running the codes.

data/svhn: http://ufldl.stanford.edu/housenumbers/train_32x32.mat, http://ufldl.stanford.edu/housenumbers/test_32x32.mat

data/cub200: http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz

data/dogs: http://vision.stanford.edu/aditya86/ImageNetDogs/images.tar, http://vision.stanford.edu/aditya86/ImageNetDogs/annotation.tar, http://vision.stanford.edu/aditya86/ImageNetDogs/lists.tar

data/ILSVRC2012: http://image-net.org/download-images

data/Places365: http://places2.csail.mit.edu/download.html

Usage Example

CIFAR10

python cifar.py -b 128 --threshold-1 0.9 --threshold-2 0.6

ImageNet

python imagenet.py data/ILSVRC2012 -b 256 --threshold-1 0.9 --threshold-2 0.6