Welcome to the DeepDetect platform!

The DD platform simplifies best practices for Deep Learning / Machine Learning. As applied researchers and engineneers, we use it at Jolibrain. As it works for us, we hope it works for you.

Best Practices

We see the platform as an help for using best practices when building and testing Deep Learning models. In more details this means:

  • A selection of fully tested neural network architectures that work extremely well in a large number of cases

  • Data pipelines for training with data augmentation that are well tested, bug-free and yield excellent results in many real-world applications

  • REST API and server to use deep models in production, with error handling, UI building and testing made easy.

Our goal is to transit the best results from academia to the industry, avoiding bugs and time losses, while preserving openess and usability.


User Setup

  • Use the New Folder button

New user folder

  • work directory in JupyterLab

Work jupyter directory

  • Inside work directory

Work inside jupyter

  • Make a folder under your name

Work username directory

User setup proceeds with two steps:

  • Setup of your User Data Directory that will store all your data files

  • Setup of your JupyterLab User Directory that will store all your Python notebooks

Setup your data directory

From the main UI:

  • Click on Data

  • Go the file manager tab

  • Use the ‘New Folder’ button to create a folder for your username, e.g. JeanDupont

Setup your JupyterLab Python directory

From the main UI:

  • Click on Jupyter

  • Go to the 'work' directory