Deep Learning by Scientists,
for the Enterprise



Web UI with Jupyter Notebooks and GPU support

# Setup platform directory and architecture
export DD_PLATFORM=$HOME/deepdetect
export ARCH=cpu # gpu also available

# Install platform repository
git clone \

# Go to directory
cd ${DD_PLATFORM}/code/${ARCH}/

# Start platform docker containers
CURRENT_UID=$(id -u):$(id -g) MUID=$(id -u) \
docker-compose up -d

# Go to http://localhost:1912

Opensource Server

Portable Deep Learning REST API

# Setup server architecture
export ARCH=cpu # gpu also available

# Pull the docker image
docker pull jolibrain/deepdetect_${ARCH}

# Start the AI server
docker run -d -p 8080:8080 jolibrain/deepdetect_${ARCH}

Great teams use DeepDetect around the world

Focus on applications, AI is not a problem.

The core idea is to remove the error sources and difficulties of Deep Learning applications by providing a safe haven of commoditized practices, all available as a single core.

While the Open Source Deep Learning Server is the core element, with REST API, multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets.

See all Models

Key features

Easy setup for both development and production
Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time-series
Web UI for training & managing models
Fast Server written in pure C++, a single codebase for Cloud, Desktop & Embedded
Training in a few hours and with small data thanks to 25+ pre-trained models
Neural network templates for the most effective architectures for GPU, CPU and Embedded devices
Comes with ready-to-use models for a range of tasks, from object detection to OCR and sentiment analysis
Full Open Source, with an ecosystem of tools (API clients, video, annotation, ...)