Project on 3D Object Detection using Lyft's level5 dataset. Obtained mAP of 0.045 on the private leader board on kaggle and ranked in the top 20% among all teams participated in the competition.

Training and prediction code for the kaggle competition of Lyft 3d Object Detection for autonomous vehicles

Description of the solution for the Lyft challenge https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles

27th in 2019 Lyft 3D Object Detection Kaggle Competition

Source code for Lyft 3D Object Detection (https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles)

For Kaggle competition: Lyft 3D Object Detection for Autonomous Vehicles

As a part of my Advanced Predictive Modeling coursework at McCombs, my team and I participated in a live Kaggle competition for Lyft. The project was 3D Object Detection for Autonomous vehicles and focuses on detecting objects (vehicles and humans), enabled using the UNet Modeling architecture.

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Kaggle project that aims to detect objects from Lyft 3D Object Detection for Autonomous Vehicles dataset using pretrained YOLOv3