RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model

This is the official implementation of the RadarFormer paper, based on the RODNet implementation. Please do cite our work if this repository helps your research.

@InProceedings{10.1007/978-3-031-31435-3_23,
author="Dalbah, Yahia
and Lahoud, Jean
and Cholakkal, Hisham",
editor="Gade, Rikke
and Felsberg, Michael
and K{\"a}m{\"a}r{\"a}inen, Joni-Kristian",
title="RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model",
booktitle="Image Analysis",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="341--358",
}

This code is heavily reliant on the RODNet repository.

Installation

Clone RadarFormer code.

cd $RadarFormer
git clone https://github.com/YahiDar/RadarFormer.git

Create a conda environment for RadarFormer using the provided env.yml file.

conda env create -n RadarFormer --file env.yml
conda activate RadarFormer

After that, clone the cruw-devkit repository into the same directory and install it using

git clone https://github.com/yizhou-wang/cruw-devkit
cd cruw-devkit
pip install .
cd ..

Then setup the RODNet package by:

pip install -e .

To run the MaXViT based model, you need to install the code from the MaxViT Repository through:

pip install git+https://github.com/ChristophReich1996/MaxViT

NOTE: This environment does NOT include the bare minimum required libraries, and includes libraries used in further research that will be published soon. A link to that research will be provided.

Prepare the dataset

Edit and run the prepare_dataset.sh file with desired directories.

Train models

Either edit and run the train.sh file with desired directories, or use:

python tools/train.py --config configs/<CONFIG_FILE> \
        --data_dir data/<DATA_FOLDER_NAME> \
        --log_dir checkpoints/

You might need to change line 152 in the .\rodnet\datasets\loaders\CRDataset.py file based on data directory.

Testing models

Either edit and run the test.sh file with desired directories, or use:

python tools/test.py --config configs/<CONFIG_FILE> \
        --data_dir data/<DATA_FOLDER_NAME> \
        --checkpoint <CHECKPOINT_PATH> \
        --res_dir results/

You might need to change the base_root/data_root/anno_root paths in the config files.

IMPORTANT NOTE:

The test split annotations are NOT provided, and you have to use the online RODNet challenge website to test it.

To do so, you have to export the annotations (output of the test.py file) using the ./tools/format_transform/convert_rodnet_to_rod2021.py file. The output must be zipped and uploaded to the challenge website.

The test.sh file automates the process.

The pretrained MaXViT model weights can be downloaded from:

https://mbzuaiac-my.sharepoint.com/:u:/g/personal/yahia_dalbah_mbzuai_ac_ae/EZ5RVt7nrK5OgozBs200hDQBIZqsGdf2bkJrwEE2jQ4KOw?e=cjf0qo