The deepdetect from hhy5277

DeepDetect : Open Source Deep Learning Server & API

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
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, ...

Support

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 platform with support is Ubuntu 14.04 LTS.

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

docker images for CPU and GPU machines are available at https://hub.docker.com/r/beniz/deepdetect_cpu/ and https://hub.docker.com/r/beniz/deepdetect_gpu/ respectively. See https://github.com/beniz/deepdetect/tree/master/docker/README.md for details on how to use them.
For Amazon AMI see https://github.com/beniz/deepdetect/issues/5#issuecomment-199952341 and performance report

Quickstart

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

Tutorials

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/

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

Documentation

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

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
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

Cmake > 3
Bazel

Caffe version

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

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.

Demo

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

Examples

List of examples, from MLP for data, text, multi-target regression to CNN and GoogleNet, finetuning, etc...: http://www.deepdetect.com/overview/examples/

Models

	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%
VOC0712 (object detection)	Y	N	https://github.com/weiliu89/caffe/tree/ssd	71.2 mAP

More models:

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/

Authors

DeepDetect is designed and implemented by Emmanuel Benazera [email protected].

Build

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 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 ..
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

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:

cmake .. -DUSE_XGBOOST=ON

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:

cmake .. -DUSE_TF=on -DUSE_XGBOOST=ON

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

Run tests with:

ctest

Start the server

cd build/main
./dede

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 0.0.0.0 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 http://www.deepdetect.com/tutorials/tutorials/

References

DeepDetect (http://www.deepdetect.com/)
Caffe (https://github.com/BVLC/caffe)
XGBoost (https://github.com/dmlc/xgboost)

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