  This paper provides a report on our solution including model selection,
tuning strategy and results obtained for Global Road Damage Detection
Challenge. This Big Data Cup Challenge was held as a part of IEEE International
Conference on Big Data 2020. We assess single and multi-stage network
architectures for object detection and provide a benchmark using popular
state-of-the-art open-source PyTorch frameworks like Detectron2 and Yolov5.
Data preparation for provided Road Damage training dataset, captured using
smartphone camera from Czech, India and Japan is discussed. We studied the
effect of training on a per country basis with respect to a single
generalizable model. We briefly describe the tuning strategy for the
experiments conducted on two-stage Faster R-CNN with Deep Residual Network
(Resnet) and Feature Pyramid Network (FPN) backbone. Additionally, we compare
this to a one-stage Yolov5 model with Cross Stage Partial Network (CSPNet)
backbone. We show a mean F1 score of 0.542 on Test2 and 0.536 on Test1 datasets
using a multi-stage Faster R-CNN model, with Resnet-50 and Resnet-101 backbones
respectively. This shows the generalizability of the Resnet-50 model when
compared to its more complex counterparts. Experiments were conducted using
Google Colab having K80 and a Linux PC with 1080Ti, NVIDIA consumer grade GPU.
A PyTorch based Detectron2 code to preprocess, train, test and submit the Avg
F1 score to is made available at https://github.com/vishwakarmarhl/rdd2020
