ROAD-R Challenge

This repository contains code for the first task of the ROAD-R Challenge. The code is built on top of 3D-RetinaNet for ROAD.

The first task requires developing models for scenarios where only little annotated data is available at training time. More precisely, only 3 out of 15 videos (from the training partition train_1 of the ROAD-R dataset) are used for training the models in this task. The videos' ids are: 2014-07-14-14-49-50_stereo_centre_01, 2015-02-03-19-43-11_stereo_centre_04, and 2015-02-24-12-32-19_stereo_centre_04.

Table of Contents

• Dependencies and data preparation
• Training
• Testing
• Post-processing

Dependencies and data preparation

For the dataset preparation and packages required to train the models, please see the Requirements section from 3D-RetinaNet for ROAD.

To download the pretrained weights, please see the end of the Performance section from 3D-RetinaNet for ROAD.

Also a video sample was given for the full road data annotation

GPU USAGE

Go through this repo for downloading all the necessary dataset.

Some Error and User Warning that might cause to crash the environment.

• Change all the datatype from _np.int to "int"__ for getting rid of attribute error [ROAD-R-2023-Challenge/data/datasets.py]

For avoiding UserWarning

• ROAD-R-2023-Challenge/modules/box_utils.py (Line 368-371) Change it into

        xx1 = x1.index_select(0, idx)
        yy1 = y1.index_select(0, idx)
        xx2 = x2.index_select(0, idx)
        yy2 = y2.index_select(0, idx)