Serverless C++ Integration

Overview DeepDetect can be linked against in order to directly use a DeepDetect object in your own C++ code, instead of using the server API. We show here an example to adapt to your needs. WARNING: this example builds only the caffe backends into deepdetect library, you should adapt library compilation (and dependencies). Compiling deepdetect as a library go to select quickstart -> server select ubuntu, build from source select compute and target GPU select caffe backend, deselect others

Similarity search

Overview DeepDetect Server and Platform come with image and object similarity search built-in. DeepDetect supports two type of image similarity analysis: Image global similarity search: indexing, search & similarity over full images. This capability is simple and mainstream since the emergence of deep neural networks for images. Objects in image similarity search: indexing, search & similarity over detected objects within images. This capability is less commonly made available than the one above and is more powerful: it allows to build search engines for objects that are within the images, as opposed to search of globally similar images.

List of Machine Learning models

Models Models are provided for image and text classification. Generic image models These models are pre-trained on the 1000 classes of the ImageNet ILSVRC Challenge. They can be used a is or finetuned to more targeted applications. 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.

OCR in the wild

DeepDetect Server and Platform come with everything ready to setup your own OCR solution. This application page describe how to get running in minutes. An example of the final output: Results on Paris street sign.
This OCR solution works in two main steps: Text detection from images uses a word detection deep learning model that outputs bounding boxes around text OCR uses a multi-word deep learning model that takes the relevant image crops from the previous step as input and predicts a text string For specific applications you may have to train your own OCR model for best results, while the text detection model remains pretty generic.

Content Moderation

Content moderation use cases from DeepDetect customers Text & image moderation on high profile dating sites Image filtering for user-generated content Selecting best images for marketing purposes GDPR compliance with automated data cleansing Objective Detection of inappropriate content is a mandatory requirement for most content platforms. Usually we refer to the detection of NOK (non-OK) images or text from OK content. Below we show how DeepDetect is used to filter both text and images by leaders of content moderation and digital marketing companies.

Application-Ready Deep Neural Net Models

A Set of Deep Neural Network Models for Classification Below are a range of deep neural network models that are free, even for commercial use in your applications. These models have been trained over images for a range of domains. Thus they should accomodate a range of applications, from fashion item recognition to sports and gender classification. This page lists a growing list of available models, along with information on how to use them and how they were built.

Tag images into ElasticSearch

Note: A more detailed version of this tutorial has been published on Elasticsearch’s blog This tutorial sets a classification service that distinguishes among 1000 different image categories, from ‘ambulance’ to ‘paddlock’, and indexes images with their categories into an instance of ElasticSearch. For every image, the DeepDetect server can directly post and index the predicted categories into ElasticSearch. This means there’s no need for glue code in between the deep learning server and ElasticSearch.

Character-Based Deep Convolutional Models

Below are a range of character-based deep convolutional neural networks that are free, even for commercial use in your applications. These models have been trained over various corpuses, from sentiment analysis in many languages to advertizing link classification from just reading a URL. They should accomodate a range of applications. Training your own models is made easy too and can lead to even more avenues. Tips and tricks for training are included at the bottom of this page.

List of Applications