This is solution for the SpaceNet three challenge as submitted to be checked by the Topcoder team. This will give you some information about TopCoder platform.

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1 Hardware requirements

Training

• 6+ core modern CPU (Xeon, i7) for fast image pre-processing;
• The models were trained on 2 * GeForce 1080 Ti;
• Training time on my setup ~ 3 hours for models with 8-bit images as inputs;
• Disk space - ~30GB should be more than enough for the Docker image + files;
• The dataset weighs ~100-150 GB and is copied, so make some room;

Inference

• 6+ core modern CPU (Xeon, i7) for fast image pre-processing;
• On 2 * GeForce 1080 Ti inference takes 3-5 minutes;
• Graph creation takes 5-10 minutes;

2 Following the Topcoder requirements

Data download guide from the authors.

Final testing guide from the authors.

Steps to reproduce the result as per the guide:

• You can clone the repository to see the code for yourself git clone REPO_URL .;

• Download the .zip file to some folder;

• Download the Dockerfile to the same folder;

• Building and running the container (assuming nvidia-docker2):

docker build -t aveysov .

docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -it -v /path/to/data:/home/keras/notebook/data -p 8888:8888 -p 6006:6006 --shm-size 8G aveysov

Jupyter notebook is launched under port 8888. Port 6006 is for tensorboard to monitor the training process, which is optional.

docker exec -it --user root 09654f4db9f9 /bin/bash

• Inside of the container you can invoke

sh train.sh - to train the model. Training for 1 epoch replaces the weights;

sh test.sh - to test the model and generate the linestrings;