DeepDetect : Open Source API & Deep Learning Server

DeepDetect (http://www.deepdetect.com/) 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.

Main functionalities:

DeepDetect implements support for supervised deep learning of images and other data, with focus on simplicity and ease of use, test and connection into existing applications.

Quickstart

Setup an image classifier API service in a few minutes: http://www.deepdetect.com/tutorials/imagenet-classifier/

Features and Documentation

Current features include:

• high-level API for machine learning
• JSON commnunication format
• remote Python client library
• dedicated server with support for asynchronous training calls
• high performances, benefit from multicores and GPU
• connector to handle large collections of images
• connector to handle CSV files with preprocessing capabilities
• connector to handle text files
• 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, convnet, mlp, logistic regression)

Documentation:

• Full documentation is available from http://www.deepdetect.com/overview/introduction/
• API documentation is available from http://www.deepdetect.com/api/

Dependencies:

• 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
• 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

Caffe version:

By default DeepDetect automatically relies on a modified version of Caffe, https://github.com/beniz/caffe/tree/master_dd_integ

Implementation:

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.

Visual Demo:

HTML and javascript classification image demo in demo/imgdetect

Authors

DeepDetect is designed and implemented by Emmanuel Benazera beniz@droidnik.fr.

Build

Below are instructions for Linux systems.

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 libcurlpp-dev libcurl4-openssl-dev protobuf-compiler libopenblas-dev libhdf5-dev libprotobuf-dev libleveldb-dev libsnappy-dev liblmdb-dev

For compiling along with Caffe:

mkdir build
cmake ..
make

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:

cmake -DBUILD_TESTS=ON ..
make

Start the server

cd build/main
./dede

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

Run examples

See tutorials from http://www.deepdetect.com/tutorials/tutorials/

References

• DeepDetect (http://www.deepdetect.com/)
• Caffe (https://github.com/BVLC/caffe)