This repo contains Pytorch implementation of depth estimation deep learning network based on the published paper: FastDepth: Fast Monocular Depth Estimation on Embedded Systems
This repository was part of the "Autonomous Robotics Lab" in Tel Aviv University
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
This code was tested with:
- Ubuntu 18.04 with python 3.6.9
The code also runs on Jetson TX2, for which all dependencies need to be installed via NVIDIA JetPack SDK.
In order to set the virtual environment, apriori installation of Anaconda3 platform is required.
Use the following commands to create a new working virtual environment with all the required dependencies.
GPU based enviroment:
git clone https://github.com/tau-adl/FastDepth
cd FastDepth
pip install -r pip_requirements.txt
Download the preprocessed NYU Depth V2 dataset in HDF5 format and place it under a data folder outside the repo directory. The NYU dataset requires 32G of storage space.
./DataCollect
python3 main.py -train -p 100 --epochs 20
python3 main.py --evaluate /home/usr/results/trained_model.pth.tar
- Sunny Yehuda - [email protected]
- Gil Rafalovich - [email protected]
- David Sriker - [email protected]