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.
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.
The reference platform with support is Ubuntu 14.04 LTS.
Setup an image classifier API service in a few minutes: http://www.deepdetect.com/tutorials/imagenet-classifier/
List of tutorials, training from text, data and images, setup of prediction services, and export to external software (e.g. ElasticSearch): http://www.deepdetect.com/tutorials/tutorials/
Current features include:
- high-level API for machine learning
- JSON communication 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 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
- 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)
- Full documentation is available from http://www.deepdetect.com/overview/introduction/
- API documentation is available from http://www.deepdetect.com/api/
- 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);
- CUDA 7 or 6.5 is required for GPU mode.
- BLAS via ATLAS, MKL, or OpenBLAS.
- protobuf
- IO libraries hdf5, leveldb, snappy, lmdb
By default DeepDetect automatically relies on a modified version of Caffe, https://github.com/beniz/caffe/tree/master_dd_integ
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.
HTML and javascript classification image demo in demo/imgdetect
- List of examples, from MLP for data, text, multi-target regression to CNN and GoogleNet, finetuning, etc...: http://www.deepdetect.com/overview/examples/
- List of free, even for commercial use, deep neural nets for image classification, and character-based convolutional nets for text classification: http://www.deepdetect.com/applications/list_models/
DeepDetect is designed and implemented by Emmanuel Benazera beniz@droidnik.fr.
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.
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 libutfcpp-dev
For compiling along with Caffe:
mkdir build
cmake ..
make
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
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
cd build/main
./dede
DeepDetect [ commit 73d4e638498d51254862572fe577a21ab8de2ef1 ]
Running DeepDetect HTTP server on localhost:8080
See tutorials from http://www.deepdetect.com/tutorials/tutorials/
- DeepDetect (http://www.deepdetect.com/)
- Caffe (https://github.com/BVLC/caffe)