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Environment\n\n- Python 3.6.5\n- CUDA 10.1.243\n\n### 1. Preparation\n\n#### 1.1 install Python packages\n\n```bash\n$ pip install -r docker/requirements.txt\n```\n\n#### 1.2 Download dataset\n\nUse kaggle API command.\n\n```text\ninput\n├── sample_submission.csv\n├── test_images\n├── train.csv\n└── train_images\n```\n\n\n#### 1.3 Split kfolds\n\n```bash\n$ cd src\n$ python data_process/s01_make_kfold_csv.py --kfold 5\n```\n\n\n### 2. How to train\n\n```bash\n$ cd src\n$ python train.py --config configs/model063.yaml --kfold 1\n$ python train.py --config configs/model063.yaml --kfold 2\n$ python train.py --config configs/model063.yaml --kfold 3\n$ python train.py --config configs/model063.yaml --kfold 4\n$ python train.py --config configs/model063.yaml --kfold 5\n$ python train.py --config configs/model064.yaml --kfold 1\n$ python train.py --config configs/model064.yaml --kfold 2\n$ python train.py --config configs/model064.yaml --kfold 3\n$ python train.py --config configs/model064.yaml --kfold 4\n$ python train.py --config configs/model064.yaml --kfold 5\n```\n\n### 3. How to predict test\n\n```bash\n$ cd src\n$ python submission.py --kfolds 12345 --config configs/model063.yaml --use-best --save-predicts\n$ python submission.py --kfolds 12345 --config configs/model064.yaml --use-best --save-predicts\n$ python utils/make_mean_sub.py\n$ ls ../output/final\nsub_model063064_kfolds12345_top062_minarea16250-13750-10000-5000_bottom042_usebest_6364avg_thres.csv\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyukkyo%2Fkaggle-understanding-clouds-69th-solution","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyukkyo%2Fkaggle-understanding-clouds-69th-solution","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyukkyo%2Fkaggle-understanding-clouds-69th-solution/lists"}