Radar Semantic Segmentation using CARRADA

Unofficial implementation of the Radar Semantic Segmentation Network (RSS-Net) presented by Kaul et al. in their paper.

The architecture has been slightly changed to match the CARRADA dataset.

The CARRADA dataset is available on Arthur Ouaknine's personal web page at this link: https://arthurouaknine.github.io/codeanddata/carrada.

Installation

Clone the repo:

$ git clone https://github.com/ArthurOuaknine/RSS-Net.git

Install this repository and the dependencies using pip and conda:

$ cd /path/to/rss-net/repo
$ pip install -e
$ pip install -r requirements.txt

Download the CARRADA Dataset here
Optional. To uninstall this package, run:

$ pip uninstall rss-net

Running the code

In any case, it is mandatory to specify both the path where the CARRADA dataset is located and the path to store the logs and models. Example: I put the Carrada folder in /datasets, the path I should specify is /datasets. The same way if I store my logs in /root/logs. Please run the following command lines while adapting the paths to your settings:

$ cd rss-net/rssnet/utils/
$ python set_paths.py --carrada /datasets --logs /root/logs

Training

In order to train a model, a JSON configuration file should be set. An example is proposed in model_configs/rss_config.json. Then execute the following command lines for training:

$ cd rss-net/rssnet/
$ python train.py --cfg model_configs/rssnet_config.json

Testing

To test a recorded model, you should specify the path to the configuration file recorded in your log folder during training. Per example, if you want to test a model segmenting range-angle representation and you log path has been set to /root/logs, you should specify the following path: /root/logs/carrada/rssnet/range_angle/name_of_the_model/config.json. This way, execute the following command lines:

$ cd rss-net/rssnet/
$ python test.py --cfg /root/logs/carrada/rssnet/range_angle/name_of_the_model/config.json

Note: the current implementation of this script will generate qualitative results in your log folder. You can disable this behavior by setting get_quali=False in the parameters of the predict() method of the Tester() class.