https://github.com/durandtibo/wildcat.pytorch
PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017
https://github.com/durandtibo/wildcat.pytorch
convnet cvpr-2017 cvpr17 cvpr2017 deep-learning image-classification pytorch weakly-supervised-learning
Last synced: 2 months ago
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PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017
- Host: GitHub
- URL: https://github.com/durandtibo/wildcat.pytorch
- Owner: durandtibo
- License: mit
- Created: 2017-07-07T13:05:46.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-08-14T17:37:24.000Z (almost 2 years ago)
- Last Synced: 2025-04-02T09:06:40.351Z (2 months ago)
- Topics: convnet, cvpr-2017, cvpr17, cvpr2017, deep-learning, image-classification, pytorch, weakly-supervised-learning
- Language: Python
- Homepage:
- Size: 17.6 KB
- Stars: 266
- Watchers: 14
- Forks: 61
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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
```sh
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
```sh
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