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

// We move old version to ./v0/

1.0. Package

• install requirements

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

  pytorch model implementation timm

  recommand anaconda

  recommand weights and biases

  deepspeed // future works

1.1. Dataset

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

• CUB-200-2011
• NA-Birds

1.2. Our pretrained model

• our pretrained model in https://idocntnu-my.sharepoint.com/:f:/g/personal/81075001h_eduad_ntnu_edu_tw/EkypiS-W0SFDkxnHN1Imv5oBPgoRblDgW8wHuVA0c6Ka7Q?e=FhBLDC
• cub200 and nabird dataset: https://idocntnu-my.sharepoint.com/:f:/g/personal/81075001h_eduad_ntnu_edu_tw/EoBb2gijwclEulDGxv_hOtIBeKuV3M6qy3IGIGMhm-jq0g?e=tcg6tm
• 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 !!

1.3. OS

• Windows10
• Ubuntu20.04
• macOS (CPU only)

2. Train

• Single GPU Training
• DataParallel (single machine multi-gpus)
• DistributedDataParallel

(more information: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)

2.1. data

train data and test data structure:

├── tain/
│   ├── class1/
│   |   ├── img001.jpg
│   |   ├── img002.jpg
│   |   └── ....
│   ├── class2/
│   |   ├── img001.jpg
│   |   ├── img002.jpg
│   |   └── ....
│   └── ....
└──

2.2. configuration

you can directly modify yaml file (in ./configs/)

2.3. run

python main.py --c ./configs/CUB200_SwinT.yaml

model will save in ./records/{project_name}/{exp_name}/backup/

2.4. about costom model

Building model refers to ./models/builder.py
More detail in how_to_build_pim_model.ipynb

2.5. multi-gpus

comment out main.py line 66

model = torch.nn.DataParallel(model, device_ids=None)

2.6. automatic mixed precision (amp)

use_amp: True, training time about 3-hours.
use_amp: False, training time about 5-hours.

3. Evaluation

If you want to evaluate our pretrained model (or your model), please give provide configs/eval.yaml (or costom yaml file is fine)

3.1. please check yaml

set yaml (configuration file)

../data/eval/ folder structure:

├── eval/
│   ├── class1/
│   |   ├── img001.jpg
│   |   ├── img002.jpg
│   |   └── ....
│   ├── class2/
│   |   ├── img001.jpg
│   |   ├── img002.jpg
│   |   └── ....
│   └── ....
└──

3.2. run

python main.py --c ./configs/eval.yaml

results will show in terminal and been save in ./records/{project_name}/{exp_name}/eval_results.txt

4. HeatMap

python heat.py --c ./configs/CUB200_SwinT.yaml --img ./vis/001.jpg --save_img ./vis/001/

5. Infer

If you want to reason your picture and get the confusion matrix, please give provide configs/eval.yaml (or costom yaml file is fine)

5.1. please check yaml

set yaml (configuration file)

../data/eval/ folder structure:

├── eval/
│   ├── class1/
│   |   ├── img001.jpg
│   |   ├── img002.jpg
│   |   └── ....
│   ├── class2/
│   |   ├── img001.jpg
│   |   ├── img002.jpg
│   |   └── ....
│   └── ....
└──

5.2. run

python infer.py --c ./configs/eval.yaml

results will show in terminal and been save in ./records/{project_name}/{exp_name}/infer_results.txt

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.