A PyTorch implementation of WS-DAN (Weakly Supervised Data Augmentation Network) for FGVC (Fine-Grained Visual Classification). (Hu et al., "See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification", arXiv:1901.09891)

NOTICE: This is NOT an official implementation by authors of WS-DAN.

The framework introduce an attention based method for extracting more detailed features and more object's parts by Attention Cropping and Attention Dropping, see Fig 1.

This code repo contains WS-DAN with feature extractors including VGG19, ResNet(34, 50, 101, 152), and Inception_v3 in PyTorch form. The default feature extractor is Inception_v3, and this can be modified conveniently in train_wsdan.py:

# feature_net = vgg19_bn(pretrained=True)
# feature_net = resnet101(pretrained=True)
feature_net = inception_v3(pretrained=True)

net = WSDAN(num_classes=num_classes, M=num_attentions, net=feature_net)

1. git clone this repo.
2. Prepare image data and rewrite dataset.py for your CustomDataset.
3. $ nohup python3 train_wsdan.py -j <num_workers> -b <batch_size> --sd <save_ckpt_directory> (etc.) 1>log.txt 2>&1 & (see train_wsdan.py for more training options)
4. $ tail -f log.txt for logging information.