wildcat.pytorch's Introduction

wildcat.pytorch

PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017 (http://webia.lip6.fr/~durandt/pdfs/2017_CVPR/Durand_WILDCAT_CVPR_2017.pdf)

Requirements

Please, install the following packages

numpy
torch
torchnet
torchvision
tqdm

Options

k: number of regions for the spatial pooling. If k is larger than 1, k is the number of regions, otherwise k is the proportion of selected regions. k=0.2 means that 20% of the regions are used.
maps: number of maps for each class
alpha: weight for minimum regions
lr: learning rate
lrp: factor for learning rate of pretrained layers. The learning rate of the pretrained layers is lr * lrp
batch-size: number of images per batch
image-size: size of the image
epochs: number of training epochs

Demo VOC 2007

python3 -m wildcat.demo_voc2007 ../data/voc --image-size 448 --batch-size 16 --lrp 0.1 --lr 0.01 --epochs 20 --k 0.2 --maps 8 --alpha 0.7

Demo MIT67

python3 -m wildcat.demo_mit67 ../data/mit67 --image-size 448 --batch-size 16 --lrp 0.1 --lr 0.001 --epochs 20 --k 0.4 --maps 8

Citing this repository

If you find this code useful in your research, please consider citing us:

@inproceedings{Durand_WILDCAT_CVPR_2017,
author = {Durand, Thibaut and Mordan, Taylor and Thome, Nicolas and Cord, Matthieu},
title = {{WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation}},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2017}
}