Awesome

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.

framework

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:

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)

Key	Value	Description
train_root	~	set value to ~ (null) means this is not in training mode.
val_root	../data/eval/	path to validation samples
pretrained	./pretrained/best.pt	pretrained model path

../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/

visualization visualization2

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