This is an experimental project aimed to build railway segmentation model using RailSem19 dataset.

It's built upon PyTorch segmentation_models_pytorch package, DeepLabV3Plus model is currently been used.

Basically, it's just few short scripts to train this model and examine how it works, but it may be usefull as a start for more advanced projects.

Here is how video processing looks like:

UPD: Added experimental feature to detect railway obstacles (with single obstacle class and on the still images only, -x command-line option):

Linux (Ubuntu 22 or whatever), NVIDIA CUDA 11.x.

If there are other CUDA versions installed, check for compatible PyTorch version and correct requirements.txt.

I didn't test it on Windows, but I suppose it should work as is.

Using venv and pip:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

To train the model use train.py script (see train.py --help for help on command-line switches). Trained model weights are saved into ./weights directory.

To view model results, use test.py script either with images (-i switch) or videos (-v switch). Again, see test.py --help for help on command-line switches.