Open Source Data Science (OSDS) Monocular Depth Estimation – Turn 2d photos into 3d photos – show your grandma the awesome results.
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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.
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.
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!
🐶