2023 AICOSS 위성 이미지 다중 분류 해커톤

This is the 2023 AICOSS Hackathon '무적환공' Team co-hosted by University of Seoul and Hyundai XiteSolution.

Team information

• 김창현(Chang-Hyun Kim)
  • B.S in University of Seoul, Dept of Environmental Engineering and Big Data Analysis.
  • M.S in University of Seoul, Dept of Statistics Data Science
• 정의수(Eui-Soo Jung) [author]
  • B.S in University of Seoul, Dept of Environmental Engineering and Big Data Analysis.

Update

01/24/2024 : we have uploaded the code

Informations

• OS : ubuntu 20.04
• python : 3.8
• CUDA : 11.4
• NVIDIA Driver version : 470,82,01
• GPU : NVIDIA Geforce rtx3090 (24GB)
• Random Seed : 605

Requirements

We didn't make requirements.txt, so I attached a separate version.

• pytorch : 1.13.1+cu117
• torchvision : 0.14.1+cu117
• Other libraries(numpy, pandas, timm etc.) are up to date.

Dataset Preparation

Download the satellite image dataset from [here] (https://dacon.io/competitions/official/236201/data)

The dataset folder should have the following below structure:

 └── data
     |
     ├── test (folder)
     ├── train (folder)
     ├── sample_submission.csv
     ├── test.csv
     └── train.csv

• There are test images (43,665) in the data/test folder.
• There are train images (65,496) in the data/train folder.

Training

All you need to do is run main.py.

 python3 main.py

• Finally, after learning is completed, 7 csv will be created to match the config in the results folder. [updated_submission_{version+1}.csv]
• After that, a csv with 7 csv soft voted(ensembled) will be created in the folder. [ensemble_results.csv]
• 7 models of weight files are created in the weights folder. [model_config_{version+1}.pth]

'augmentation' folder and 'multi_augmentation.py' were only used for experiments and not in the learning process!

• augmentation folder : This is a baseline code for data synthesis through GAN and active learning in next research.
• multi_augmentation.py : This is augmentation by crop with small kernel(crop_size : 94, stride : 47)

Results

(Loss : BCELogitLoss / Evaluation Score : mAP)

Competition Results

• Public : 2nd
• Private : 5th
• Final : 3rd (서울시립대학교 공과대학장상 2등상)

Reference

[1] TAN, Mingxing; LE, Quoc. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, 2019. p. 6105-6114.
[2] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017.
[3] Foret, Pierre, et al. "Sharpness-aware minimization for efficiently improving generalization." arXiv preprint arXiv:2010.01412 (2020).
[4] Zhang, Hongyi, et al. "mixup: Beyond empirical risk minimization." arXiv preprint arXiv:1710.09412 (2017).

Presentation Materials

We would appreciate it if you could refer to this pdf. 2023 AICOSS 무적환공.pdf