Awesome
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. Ifk
is larger than 1,k
is the number of regions, otherwisek
is the proportion of selected regions.k=0.2
means that 20% of the regions are used.maps
: number of maps for each classalpha
: weight for minimum regionslr
: learning ratelrp
: factor for learning rate of pretrained layers. The learning rate of the pretrained layers islr * lrp
batch-size
: number of images per batchimage-size
: size of the imageepochs
: 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}
}
Licence
MIT License