README

Mask Image Classification

Code for solution in Mask Image Classification hosted by Naver Boostcamp AI Tech.

To learn more detail about the competition, please, refer to the AI Stage post

Archive contents

minibatch28/
├── data/
|   ├── image/
|   |   ├── train/ 
│   |   |   ├── 00001_male_Asian_40/
│   |   |   |   ├── mask.jpg
│   |   |   |   ├── mask2.jpg
│   |   |   |   ├── incorrect.png 
│   |   |   |   └── normal.jpeg 
│   |   |   ├── {Number}_{Gender}_{Race}_{Age}/
│   |   |   |   ├── ...
│   |   |   |   └── ...
|   |   |   └── 99999_female_Asian_150/
│   |   |       ├── mask.jpg 
│   |   |       ├── incorrect.jpg
│   |   |       └── normal.jpg 
|   |   └── eval/ 
│   |       ├── abcde.jpg
│   |       ├── {Any_image_name}.jpg
│   |       └── lorem_ipsum.jpeg 
│   ├── train.csv
│   └── info.csv
├── output/
│   └── ensemble/ 
├── models/
├── dataset.py
├── loss.py
├── inference.py
├── train.py
└── train.sh

• data/ : contains raw data dir and label data (should contain 'train.csv', 'info.csv')
• data/image/ : raw image dir of the competition
• data/eval/ : evaluation image dir of the competition
• output/ : inference result csv files will be created
• output/ensemble/ : ensemble result csv files will be created
• models/ : contains trained state_dict of each model

Requirements

• Ubuntu 18.04.5 LTS
• Python 3.8.5
• Pytorch 1.7.1
• CUDA 11.0

You can use the pip install -r requirements.txt to install the necessary packages.

Hardware

• CPU: 8 x Intel(R) Xeon(R) Gold 5120 CPU @ 2.20GHz
• GPU: 1 x Tesla V-100
• RAM: 88G

Prepare Data

You can automatically generate data/train_list.csv, data/valid_list.csv files by running train.py

Train Model

To train model, run following command.

$ python train.py --model {model_number} --dataset {model_number} --batch_size {batch_size} --epochs {epochs} \

    --lr_decay_step {lr_decay_step} --gamma {gamma} --lr {learning_rate} --scheduler 1 \

    --cutmix 0 --criterion {model_number} --optimizer {optimizer}

To train 5 models at once, run following shell script file.

$ ./train.sh

Predict

If trained weights are prepared, you can create files that contains class of images.

$ python inference.py --model {model_number} --batch_size {batch_size}

Ensemble

To inference and ensemble 5 models at once, run following shell script file.

$ ./ensemble.sh

Then ensemble.csv will be created in output/ensemble directory.

Members of Minibatch 28

• 강재현_T2003
• 권예환_T2012
• 김준태_T2058
• 박마루찬_T2078
• 서광채_T2106
• 장동건_T2185
• 홍요한_T2244