Open Source Deep Learning Server

Open Source + Deep Learning + API + Server

Get state of the art results with no code involved	Seamless switch between development and production	Easy API and flexible template output formats
Classify images, detect objects, deal with text and numerical data from your application or the command line by series of simple calls to the deep learning server.	Use one or more deep learning servers for development and production, test, move and reuse models, it has never been easier to bring the full machine learning cycle into production!	A simple yet powerful and generic API for use of Machine Learning. It is simple to setup, test, and plug into your existing application.

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Features

Learning

Handling of large collections of images
On-the-fly image data augmentation
CSV inputs with preprocessing
SVM inputs for sparse data
Flexible text inputs
Built-in model assessment metrics

Application Building

REST API for Deep Learning
JSON communication format
Pre-trained models
Neural architecture templates
Python, Java, C# clients
Output templating

Production

High throughput CPU & GPU
Runs on Cloud, Desktop and Embedded
Asynchronous Server
Isolated Services
Error Handling for CPU & GPU

Questions, comments or difficulties ? contact@deepdetect.com

No Compromise on Technology

Embeds the best deep learning technology

The deep learning server has full support for the state of the art Caffe, XGBoost and Tensorflow libraries, with even more choice on the way. No compromise, the best image recognition and neural network technologies at your fingertips.

Makes the most out of your CPU and GPU

Multi-purpose deep learning server that supports multiple learning jobs and services in parallel. The full C++ stack is designed for genericity and the best performances.

Open Source with professional support

A full Open Source product that gives you freedom and control over your stack. The product is supported by Machine Learning experts and AI veterans, and they can help with your applications too.

Versatile Machine Learning

Templates for the best neural architectures

Deep neural networks with a proven track record are included as templates. These include Googlenet, Alexnet, ResNet, character-based nets for image and text classification.

Range of model quality assessment measures

Assessing your model quality made easy. From F1-score to multiclass log loss, measures and their history can be accessed during the learning phase.

Collection of input connectors

Handle large repositories of images with extreme ease. Massage and pre-process data from CSV files directly from the API prior to learning a statistical model.

Applications

State of the art image recognition

Make instant decisions on images, from content moderation to scene understanding.

Classification and Prediction

Use data to predict trends, sort out text, hook up to data streams and label content in real time.

Anomalies detection

From cybersecurity to fraud detection, analyze bulks of unlabeled data to detect anomalies.