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Efficientnets\n* DDP training with SyncBN and Apex mixed precision\n* AdamW with cosine annealing\n* EMA Model\n* Bitmix\n\n## Quick setup and start \n\n### Requirements \n\n*  Nvidia drivers, CUDA \u003e= 10.2, cuDNN \u003e= 7\n*  [Docker](https://www.docker.com/), [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) \n\nThe provided dockerfile is supplied to build image with cuda support and cudnn.\n\n\n### Preparations \n\n* Clone the repo, build docker image. \n    ```bash\n    git clone https://github.com/lRomul/argus-alaska.git\n    cd argus-alaska\n    make build\n    ```\n\n* Download and extract [dataset](https://www.kaggle.com/c/alaska2-image-steganalysis/data) to `data` folder.\n\n### Run\n\n* Run docker container \n```bash\nmake\n```\n\n* Create folds split and extract quality of images \n```bash\npython make_folds.py\npython make_quality_json.py\n```\n\n* Train model\n```bash\npython train.py --experiment train_001\n```\n\n* Predict test and make submission \n```bash\npython predict.py --experiment train_001\n```\n\n* Train on 4 GPUs with distributed data parallel \n```bash\n./distributed_train.sh 4 --experiment ddp_train_001\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flromul%2Fargus-alaska","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flromul%2Fargus-alaska","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flromul%2Fargus-alaska/lists"}