Open Source Deep Learning Server & API

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DeepDetect (https://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. It has support for both training and inference, with automatic conversion to embedded platforms with TensorRT (NVidia GPU) and NCNN (ARM CPU).

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

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

the deep learning libraries Caffe, Tensorflow, Caffe2, Torch, NCNN Tensorrt and Dlib
distributed gradient boosting library XGBoost
clustering with T-SNE
similarity search with Annoy and FAISS

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

Main features
Machine Learning functionalities per library
Installation
- From docker
- From source
- From Amazon AMI: GPU and CPU
- Mimic Continuous Integration testing
Models ready to use
Ecosystem
Documentation:
- Introduction
- API Quickstart: setup an image classifier API service in a few minutes
- API Tutorials: training from text, data and images, setup of prediction services, and export to external software (e.g. ElasticSearch)
- API Reference
- Examples: MLP for data, text, multi-target regression to CNN and GoogleNet, finetuning, etc...)
- FAQ
Demos:
- Image classification Web application using HTML and javascript
- Image similarity search using python client
- Image object detection using python client
- Image segmentation using python client
Performance tools and report done on NVidia Desktop and embedded GPUs, along with Raspberry Pi 3.
References
Authors

Build type	STABLE	DEVEL
SOURCE
Docker image CPU
Docker image GPU
Docker image GPU+TORCH
Docker image GPU+TENSORRT

Main features

high-level API for machine learning and deep learning
support for Caffe, Tensorflow, XGBoost, T-SNE, Caffe2, NCNN, TensorRT, Pytorch
classification, regression, autoencoders, object detection, segmentation, time-series
JSON communication format
remote Python and Javacript clients
dedicated server with support for asynchronous training calls
high performances, benefit from multicore CPU and GPU
built-in similarity search via neural embeddings
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, ...)
range of special losses (e.g Dice, contour, ...)
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, SSD, DeepLab, PSPNet, U-Net, CRNN, ShuffleNet, SqueezeNet, MobileNet, RefineDet, VOVNet, ...)
support for sparse features and computations on both GPU and CPU
built-in similarity indexing and search of predicted features, images, objects and probability distributions

Machine Learning functionalities per library

	Caffe	Caffe2	XGBoost	TensorRT	NCNN	Libtorch	Tensorflow	T-SNE	Dlib
Serving
Training (CPU)	Y	Y	Y	N/A	N/A	Y	N	Y	N
Training (GPU)	Y	Y	Y	N/A	N/A	Y	N	Y	N
Inference (CPU)	Y	Y	Y	N	Y	Y	Y	N/A	Y
Inference (GPU)	Y	Y	Y	Y	N	Y	Y	N/A	Y

Models
Classification	Y	Y	Y	Y	Y	Y	Y	N/A	Y
Object Detection	Y	Y	N	Y	Y	N	N	N/A	Y
Segmentation	Y	N	N	N	N	N	N	N/A	N
Regression	Y	N	Y	N	N	Y	N	N/A	N
Autoencoder	Y	N	N/A	N	N	N	N	N/A	N
NLP	Y	N	Y	N	N	Y	N	Y	N
OCR / Seq2Seq	Y	N	N	N	Y	N	N	N	N
Time-Series	Y	N	N	N	Y	Y	N	N	N

Data
CSV	Y	N	Y	N	N	N	N	Y	N
SVM	Y	N	Y	N	N	N	N	N	N
Text words	Y	N	Y	N	N	N	N	N	N
Text characters	Y	N	N	N	N	N	N	Y	N
Images	Y	Y	N	Y	Y	Y	Y	Y	Y
Time-Series	Y	N	N	N	Y	N	N	N	N

Tools and Clients

Python client:
- REST client: https://github.com/jolibrain/deepdetect/tree/master/clients/python
- 'a la scikit' bindings: https://github.com/ArdalanM/pyDD
Javacript client: https://github.com/jolibrain/deepdetect-js
Java client: https://github.com/kfadhel/deepdetect-api-java
Early C# client: https://github.com/jolibrain/deepdetect/pull/98
Log DeepDetect training metrics via Tensorboard: https://github.com/jolibrain/dd_board

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%
ResNext 50	Y	N	https://github.com/terrychenism/ResNeXt	76.9%
ResNext 101	Y	N	https://github.com/terrychenism/ResNeXt	77.9%
ResNext 152	Y	N	https://github.com/terrychenism/ResNeXt	78.7%
DenseNet-121	Y	N	https://github.com/shicai/DenseNet-Caffe	74.9%
DenseNet-161	Y	N	https://github.com/shicai/DenseNet-Caffe	77.6%
DenseNet-169	Y	N	https://github.com/shicai/DenseNet-Caffe	76.1%
DenseNet-201	Y	N	https://github.com/shicai/DenseNet-Caffe	77.3%
SE-BN-Inception	Y	N	https://github.com/hujie-frank/SENet	76.38%
SE-ResNet-50	Y	N	https://github.com/hujie-frank/SENet	77.63%
SE-ResNet-101	Y	N	https://github.com/hujie-frank/SENet	78.25%
SE-ResNet-152	Y	N	https://github.com/hujie-frank/SENet	78.66%
SE-ResNext-50	Y	N	https://github.com/hujie-frank/SENet	79.03%
SE-ResNext-101	Y	N	https://github.com/hujie-frank/SENet	80.19%
SENet	Y	N	https://github.com/hujie-frank/SENet	81.32%
VOC0712 (object detection)	Y	N	https://github.com/weiliu89/caffe/tree/ssd	71.2 mAP
InceptionBN-21k	Y	N	https://github.com/pertusa/InceptionBN-21K-for-Caffe	41.9%
Inception v3 5K	N	Y	https://github.com/openimages/dataset
5-point Face Landmarking Model (face detection)	N	N	http://blog.dlib.net/2017/09/fast-multiclass-object-detection-in.html
Front/Rear vehicle detection (object detection)	N	N	http://blog.dlib.net/2017/09/fast-multiclass-object-detection-in.html

More models:

List of free, even for commercial use, deep neural nets for image classification, and character-based convolutional nets for text classification: https://www.deepdetect.com/applications/list_models/

References

DeepDetect (https://www.deepdetect.com/)
Caffe (https://github.com/jolibrain/caffe)
XGBoost (https://github.com/dmlc/xgboost)
T-SNE (https://github.com/DmitryUlyanov/Multicore-TSNE)

Authors

DeepDetect is designed, implemented and supported by Jolibrain with the help of other contributors.

周春雷 / deepdetect

Open Source Deep Learning Server & API

Main features

Machine Learning functionalities per library

Tools and Clients

Models

References

Authors

简介

发行版

贡献者

近期动态

周春雷 / deepdetect .gitee-modal { width: 500px !important; }

Open Source Deep Learning Server & API

Main features

Machine Learning functionalities per library

Tools and Clients

Models

References

Authors

简介

发行版

贡献者

近期动态

搜索帮助

周春雷 / deepdetect