https://github.com/lromul/argus-alaska

Kaggle | Part of 25th place solution for ALASKA2 Image Steganalysis kaggle competition.
https://github.com/lromul/argus-alaska

computer-vision deep-learning kaggle kaggle-competition pytorch steganalysis

Last synced: 18 days ago
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Kaggle | Part of 25th place solution for ALASKA2 Image Steganalysis kaggle competition.

Host: GitHub
URL: https://github.com/lromul/argus-alaska
Owner: lRomul
License: mit
Created: 2020-05-05T13:24:36.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-07-23T15:07:48.000Z (almost 2 years ago)
Last Synced: 2025-04-04T23:30:08.948Z (20 days ago)
Topics: computer-vision, deep-learning, kaggle, kaggle-competition, pytorch, steganalysis
Language: Python
Homepage: https://www.kaggle.com/c/alaska2-image-steganalysis
Size: 94.7 KB
Stars: 3
Watchers: 2
Forks: 0
Open Issues: 4
Metadata Files:
- Readme: README.md
- License: LICENSE

README

# ALASKA2 Image Steganalysis

Source code of solution for [ALASKA2 Image Steganalysis](https://www.kaggle.com/c/alaska2-image-steganalysis) competition.

## Solution

Key points:
* Efficientnets
* DDP training with SyncBN and Apex mixed precision
* AdamW with cosine annealing
* EMA Model
* Bitmix

## Quick setup and start

### Requirements

* Nvidia drivers, CUDA >= 10.2, cuDNN >= 7
* [Docker](https://www.docker.com/), [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)

The provided dockerfile is supplied to build image with cuda support and cudnn.

### Preparations

* Clone the repo, build docker image.
```bash
git clone https://github.com/lRomul/argus-alaska.git
cd argus-alaska
make build
```

* Download and extract [dataset](https://www.kaggle.com/c/alaska2-image-steganalysis/data) to `data` folder.

### Run

* Run docker container
```bash
make
```

* Create folds split and extract quality of images
```bash
python make_folds.py
python make_quality_json.py
```

* Train model
```bash
python train.py --experiment train_001
```

* Predict test and make submission
```bash
python predict.py --experiment train_001
```

* Train on 4 GPUs with distributed data parallel
```bash
./distributed_train.sh 4 --experiment ddp_train_001
```