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Deep Video Analytics     Build Status Build Status

by Akshay Bhat

UI Screenshot

Deep Video Analytics is a platform for indexing and extracting information from videos and images. With latest version of docker installed correctly, you can run Deep Video Analytics in minutes locally (even without a GPU) using a single command.


Deep Video Analytics implements a client-server architecture pattern, where clients can access state of the server via a REST API. For uploading, processing data, training models, performing queries, i.e. mutating the state clients can send DVAPQL (Deep Video Analytics Processing and Query Language) formatted as JSON. The query represents a directed acyclic graph of operations.

Visual Data Network

A separate repository VisualDataNetwork/root maintains examples of DVAPQL scripts for performing tasks such as processing image dataset (e.g. COCO), Youtube videos, Twitch livestreams, training FAISS indexers etc.

Installation & Demo

Please visit



Code organization

  • /client : Python client using DVA REST API
  • /configs : ngnix config + default models + processing pipelines
  • /deploy : Dockerfiles + Instructions for development, single machine deployment and scalable deployment with Kubernetes
  • /docs : Documentation, tutorial and experiments
  • /tests : Tests, Notebooks for interactive debugging andtest data
  • /repos : Code copied from third party repos, e.g. Yahoo LOPQ, TF-CTPN etc.
  • /server : dvalib + django server contains contains bulk of the code for UI, App and models.
  • /logs : Empty dir for storing logs

Libraries present in this repository and their licenses

Library Link to the license
YAD2K MIT License
AdminLTE2 MIT License
FabricJS MIT License
Facenet MIT License
JSFeat MIT License
Insight Face MIT License
CRNN.pytorch MIT License
Original CRNN code by Baoguang Shi MIT License
Object Detector App using TF Object detection API MIT License
Plotly.js MIT License
Text Detection CTPN MIT License
SphereFace MIT License
Segment annotator BSD 3-clause
Youtube 8M feature extractor weights Apache 2.0
LOPQ Apache 2.0
Open Images Pre-trained network Apache 2.0
Interval Tree Apache 2.0

Libraries present in container (/root/thirdparty/)

Library Link to the license
faiss BSD + PATENTS License
dlib Boost Software License

Additional libraries & frameworks

License & Copyright

Copyright 2016-2018, Akshay Bhat, All rights reserved.


Deep Video Analytics is nearing stable 1.0, we expect to release in Summer 2018. The license will be relaxed once a stable release version is reached. Please contact me for more information.