This repository provides the pretrained and optimized YOLO models for detecting disaster victims in aerial imagery.
Implementation in this directory has following features
- New Object Detection Dataset for Emergency Response (ODDER)
- Pretrained YOLO models on ODDER
- Quantized YOLO models
The implementation of this repository has been tested on x86_64 Ubuntu 18.04.5 LTS 5.4.0-73-generic and Ubuntu 16.04.6 LTS 4.4.0-210-generic, as well as NVIDIA Jetson TX2 (aarch64 NVIDIA JetPack 4.4.2).
We have introduced Object Detection Dataset for Emergency Response - ODDER with two classes.
- Person
- Vehicle
ODDER contains total 3930 labelled images containing 6900 vehicles and 2100 humans in total.
ODDER can be downloaded from kaggle. Before downloading dataset from kaggle follow the following steps:
pip install kaggle
cd ~/.kaggle
Go to your kaggle.com/{username}/account and click on generate new API token to download kaggle.json
mv Downloads/kaggle.json /.kaggle/kaggle.json
cd ~/.kaggle
chmod 600 kaggle.json
Now your kaggle authentication is complete. Use the following command to download dataset for YOLOv3 and YOLOv4
kaggle datasets download -d kagglerx1/aiderdetectionyolo
Use the following command to download dataset for YOLOv5
kaggle datasets download -d maryamsana/yolov5emergencyresponse
The implementation contains code on following levels:
- tensorrt_inference: contains tensorrt accelerated engines of pretrained YOLO models
- yolov3: contains pretrained YOLOv3 and YOLO4 models
- yolov5: contains pretrained YOLOv5 models