3D Pose Estimation for Vehicles

Related tags

Deep Learning vehicle_pose_estimation
Overview

3D Pose Estimation for Vehicles

Introduction

This work generates 4 key-points and 2 key-edges from vertices and edges of vehicles as ground truth. The brightness of each generated heatmap is normal distribution. The model is combined with Resnet-50 for feature generation and three transpose-convolution layers for generation of heatmaps.

This code was adapted from an official pytorch implementation of Simple Baselines for Human Pose Estimation and Tracking. This work provides baseline methods that are surprisingly simple and effective, thus helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. On COCO keypoints valid dataset, their best single model achieves 74.3 of mAP.

Results

Training after 4 epochs (27000+ samples per epoch) image

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Comments
  • Dataset format and how to train with custom dataset

    Dataset format and how to train with custom dataset

    I know that you cannot provide dataset from my previous question. If so, can you please let me know the format of the dataset and how to train if I prepare my own dataset. Thank you again.

    opened by ilwoonam75 0
Owner
Jingyi Wang
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Jingyi Wang
GitHub
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