Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow and XGBoost
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DeepDetect : Open Source Deep Learning Server & API

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DeepDetect ( is a machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.

DeepDetect relies on external machine learning libraries through a very generic and flexible API. At the moment it has support for:

  • the deep learning library Caffe
  • distributed gradient boosting library XGBoost
  • the deep learning and other usages library Tensorflow

Machine Learning functionalities per library (current):

Training Prediction Classification Object Detection Regression Autoencoder
Caffe Y Y Y Y Y Y
XGBoost Y Y Y N Y N/A
Tensorflow N Y Y N N N

Input data support per library (current):

CSV SVM Text words Text characters Images
Caffe Y Y Y Y Y
XGBoost Y Y Y N N
Tensorflow N N N N Y

Main functionalities

DeepDetect implements support for supervised and unsupervised deep learning of images, text and other data, with focus on simplicity and ease of use, test and connection into existing applications. It supports classification, object detection, regression, autoencoders, ...


Please join either the community on Gitter or on IRC Freenode #deepdetect, where we help users get through with installation, API, neural nets and connection to external applications.

Supported Platforms

The reference platforms with support are Ubuntu 14.04 LTS and Ubuntu 16.04 LTS.

Supported images that come with pre-trained image classification deep (residual) neural nets:


Setup an image classifier API service in a few minutes:


List of tutorials, training from text, data and images, setup of prediction services, and export to external software (e.g. ElasticSearch):

Features and Documentation

Current features include:

  • high-level API for machine learning and deep learning
  • Support for Caffe, Tensorflow and XGBoost
  • classification, regression, autoencoders, object detection
  • JSON communication format
  • remote Python client library
  • dedicated server with support for asynchronous training calls
  • high performances, benefit from multicore CPU and GPU
  • connector to handle large collections of images with on-the-fly data augmentation (e.g. rotations, mirroring)
  • connector to handle CSV files with preprocessing capabilities
  • connector to handle text files, sentences, and character-based models
  • connector to handle SVM file format for sparse data
  • range of built-in model assessment measures (e.g. F1, multiclass log loss, ...)
  • no database dependency and sync, all information and model parameters organized and available from the filesystem
  • flexible template output format to simplify connection to external applications
  • templates for the most useful neural architectures (e.g. Googlenet, Alexnet, ResNet, convnet, character-based convnet, mlp, logistic regression)
  • support for sparse features and computations on both GPU and CPU
  • C++, gcc >= 4.8 or clang with support for C++11 (there are issues with Clang + Boost)
  • eigen for all matrix operations;
  • glog for logging events and debug;
  • gflags for command line parsing;
  • OpenCV >= 2.4
  • cppnetlib
  • Boost
  • curl
  • curlpp
  • utfcpp
  • gtest for unit testing (optional);
Caffe Dependencies
  • CUDA 7 or 6.5 is required for GPU mode.
  • BLAS via ATLAS, MKL, or OpenBLAS.
  • protobuf
  • IO libraries hdf5, leveldb, snappy, lmdb
XGBoost Dependencies

None outside of C++ compiler and make

Tensorflow Dependencies

Caffe version

By default DeepDetect automatically relies on a modified version of Caffe, This version includes many improvements over the original Caffe, such as sparse input data support, exception handling, class weights, object detection, and various additional losses and layers.


The code makes use of C++ policy design for modularity, performance and putting the maximum burden on the checks at compile time. The implementation uses many features from C++11.

  • Image classification Web interface: HTML and javascript classification image demo in demo/imgdetect

  • Image similarity search: Python script for indexing and searching images is in demo/imgsearch

  • Image object detection: Python script for object detection within images is in demo/objdetect

Caffe Tensorflow Source Top-1 Accuracy (ImageNet)
AlexNet Y N BVLC 57.1%
SqueezeNet Y N DeepScale 59.5%
Inception v1 / GoogleNet Y Y BVLC / Google 67.9%
Inception v2 N Y Google 72.2%
Inception v3 N Y Google 76.9%
Inception v4 N Y Google 80.2%
ResNet 50 Y Y MSR 75.3%
ResNet 101 Y Y MSR 76.4%
ResNet 152 Y Y MSR 77%
Inception-ResNet-v2 N Y Google 79.79%
VGG-16 Y Y Oxford 70.5%
VGG-19 Y Y Oxford 71.3%
ResNext 50 Y N 76.9%
ResNext 101 Y N 77.9%
ResNext 152 Y N 78.7%
VOC0712 (object detection) Y N 71.2 mAP

More models:


DeepDetect is designed and implemented by Emmanuel Benazera


Below are instructions for Ubuntu 14.04 LTS. For other Linux and Unix systems, steps may differ, CUDA, Caffe and other libraries may prove difficult to setup. If you are building on 16.04 LTS, look at that tells you how to proceed.

Beware of dependencies, typically on Debian/Ubuntu Linux, do:

sudo apt-get install build-essential libgoogle-glog-dev libgflags-dev libeigen3-dev libopencv-dev libcppnetlib-dev libboost-dev libboost-iostreams-dev libcurlpp-dev libcurl4-openssl-dev protobuf-compiler libopenblas-dev libhdf5-dev libprotobuf-dev libleveldb-dev libsnappy-dev liblmdb-dev libutfcpp-dev cmake libgoogle-perftools-dev

Default build with Caffe

For compiling along with Caffe:

mkdir build
cd build
cmake ..

If you are building for one or more GPUs, you may need to add CUDA to your ld path:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

If you would like to build with cuDNN, your cmake line should be:

cmake .. -DUSE_CUDNN=ON

If you would like a CPU only build, use:

cmake .. -DUSE_CPU_ONLY=ON

Build with XGBoost support

If you would like to build with XGBoost, include the -DUSE_XGBOOST=ON parameter to cmake:


Build with Tensorflow support

First you must install Bazel and Cmake with version > 3.

And other dependencies:

sudo apt-get install python-numpy swig python-dev python-wheel unzip

If you would like to build with Tensorflow, include the -DUSE_TF=ON paramter to cmake:

cmake .. -DUSE_TF=ON

You can combine with XGBoost support with:


Run tests

Note: running tests requires the automated download of ~75Mb of datasets, and computations may take around thirty minutes on a CPU-only machines.

To prepare for tests, compile with:


Run tests with:


Start the server

cd build/main

DeepDetect [ commit 73d4e638498d51254862572fe577a21ab8de2ef1 ]
Running DeepDetect HTTP server on localhost:8080

Main options are:

  • -host to select which host to run on, default is localhost, use to listen on all interfaces
  • -port to select which port to listen to, default is 8080
  • -nthreads to select the number of HTTP threads, default is 10

To see all options, do:

./dede --help

Run examples

See tutorials from