/IEEE-Big-Data-2020

Repository for IEEE Big Data Challenge 2020 "Road Damage Detection".

Primary LanguageJupyter NotebookMIT LicenseMIT

IEEE-Big-Data-2020

This repository contains source code and trained models for Road Damage Detection and Classification Challenge that was held as one of the 2020 IEEE Big Data Cup.

The best ensemble model achieved mean F1-score of 0.628 (Ranked 3rd) on Test 1 dataset and 0.635 (Ranked 2nd) on Test 2 dataset of the competition.

Quick-start

  1. Clone the road-damage-detection repo into $RDD:

    git clone https://github.com/kevaldoshi17/IEEE-Big-Data-2020.git

  2. Clone and install the darknet repo as shown here:

    git clone https://github.com/AlexeyAB/darknet.git

RDCC Dataset Setup

  1. Register at RDDC website to download competition dataset

  2. Augment the dataset using augment.py

  3. Generate the label files for Darknet using convert_annotations.py

NOTE: It is expected that the Dataset be structured in PASCAL VOC format (images under JPEGImages, XML files under Annotations directory)

Submission

Training

run ./darknet detector train obj.data (choose yolov_416/608.cfg) /backup/yolov4_1000.weights

The ensemble model consists of a base model and 4 semi-base models. Instead of augmenting the test set which increases the prediction time, we save similar base models trained for different number of iterations and use different file sizes in cfg to artificially augment the test data and use a multi-gpu setup to quickly run it for multiple models.

Base Model: Yolo - Aug - 416x416 it: 14000 test: 416x416

S. Model 1: Yolo - Aug - 416x416 it: [13000-20000] test: 416x416

S. Model 2. Yolo - Aug - 608x608 it: [14000-20000] test: 608x608

S. Model 3. Yolo - Aug - 608x608 it: [15000-20000] test: 416x416

S. Model 4. Yolo - No Aug - 416x416 it: [15000-18000] test: 416x416

Save each S. Model at intervals of 1000 between the limits shown above. Save all of the models in the folder model.

All the pretrained models can be downloaded from here.

Inference

For each model, run the following script to detect the bounding boxes and save them in ensemble1 or ensemble2 depending on the test dataset.

The extracted bounding boxes from each model have been submitted with the source code.

./darknet detector test obj.data yolo_608.cfg (insert model here) -thresh 0.05 -dont_show  < test.txt -out ensemble/model_name.json

Once all the bounding boxes (saved as json files) from each model is extracted (should be 33 in total per test dataset), run submission.ipynb. That should output the submitted results.