SavtaDepth

Savta Depth - Monocular Depth Estimation OSDS Project

Savta Depth is a collaborative *O*pen *S*ource *D*ata *S*cience project for monocular depth estimation.

Here you will find the code for the project, but also the data, models, pipelines and experiments. This means that the project is easily reproducible on any machine, but also that you can contribute to it as a data scientist.

Have a great idea for how to improve the model? Want to add data and metrics to make it more explainable/fair? We'd love to get your help.

Contributing Guide

Here we'll list things we want to work on in the project as well as ways to start contributing. If you'd like to take part, please follow the guide.

Setting up your environment to contribute

• To get started, fork the repository on DAGsHub
• Next, clone the repository you just forked by typing the following command in your terminal: bash $ git clone https://dagshub.com/<your-dagshub-username>/SavtaDepth.git

• To get your environment up and running docker is the best way to go. We use an instance of MLWorkspace.

  • You can Just run the following commands to get it started.

    $ chmod +x run_dev_env.sh
    $ ./run_dev_env.sh
    

  • Open localhost:8080 to see the workspace you have created. You will be asked for a token – enter dagshub_savta

  • In the top right you have a menu called Open Tool. Click that button and choose terminal (alternatively open VSCode and open terminal there) and type in the following commands to install a virtualenv and dependencies:

    $ make env
    $ source activate savta_depth
    

    Now when we have an environment, let's install all of the required libraries.

    Note: If you don't have a GPU you will need to install pytorch separately and then run make requirements. You can install pytorch for computers without a gpu with the following command:

    $ conda install pytorch torchvision cpuonly -c pytorch
    

    To install the required libraries run the following command:

    $ make load_requirements
    

• Pull the dvc files to your workspace by typing:

  $ dvc checkout #use this to get the data, models etc
  

  Note: You might need to install and setup the tools to pull from a remote. Read more in this guide on how to setup Google Storage or AWS S3 access.

• After you are finished your modification, make sure to do the following:

  • Freeze your virtualenv by typing in the terminal:

    $ make save_requirements
    

  • Push your code to DAGsHub, and your dvc managed files to your dvc remote. In order to setup a dvc remote please refer to this guide.

  • Create a Pull Request on DAGsHub!

  • 🐶

TODO:

• Web UI
• Testing various datasets as basis for training
• Testing various models for the data
• Adding qualitative tests for model performance (visually comparing 3d image outputs)