A Novel Plug-in Module for Fine-grained Visual Classification

paper url: https://arxiv.org/abs/2202.03822

We propose a novel plug-in module that can be integrated to many common backbones, including CNN-based or Transformer-based networks to provide strongly discriminative regions. The plugin module can output pixel-level feature maps and fuse filtered features to enhance fine-grained visual classification. Experimental results show that the proposed plugin module outperforms state-ofthe-art approaches and significantly improves the accuracy to 92.77% and 92.83% on CUB200-2011 and NABirds, respectively.

1. Environment setting

• install requirements
• replace folder timm/ to our timm/ folder (for ViT or Swin-T)

Prepare dataset

In this paper, we use 2 large bird's datasets:

• CUB-200-2011
• NA-Birds

Our pretrained model

Download the pretrained model from this url: https://drive.google.com/drive/folders/1ivMJl4_EgE-EVU_5T8giQTwcNQ6RPtAo?usp=sharing

• backup/ is our pretrained model path.
• resnet50_miil_21k.pth and vit_base_patch16_224_miil_21k.pth are imagenet21k pretrained model (place these file under models/), thanks to https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/MODEL_ZOO.md !!

OS

• Windows10
• Ubuntu20.04
• macOS

2. Train

configuration file: config.py

python train.py --train_root "./CUB200-2011/train/" --val_root "./CUB200-2011/test/"

3. Evaluation

configuration file: config_eval.py

python eval.py --pretrained_path "./backup/CUB200/best.pth" --val_root "./CUB200-2011/test/"

4. Visualization

configuration file: config_plot.py

python plot_heat.py --pretrained_path "./backup/CUB200/best.pth" --img_path "./img/001.png/"

Acknowledgment

• Thanks to timm for Pytorch implementation.

• This work was financially supported by the National Taiwan Normal University (NTNU) within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE) in Taiwan, sponsored by Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 110- 2221-E-003-026, 110-2634-F-003 -007, and 110-2634-F-003 -006. In addition, we thank to National Center for Highperformance Computing (NCHC) for providing computational and storage resources.