Visual Data Analytics platform

Visual Data Analytics platform

Deep Video Analytics & Visual Data Network aim to revolutionize visual data analysis by providing a comprehensive platform for storage, analysis & sharing.


Upload videos or set of images. Download Youtube urls automatically. Browse & annotate uploaded videos. Ability to import pre-indexed datasets.


Perform scene detection, frame extraction on videos. Annotate frames, detections with bounding boxes, labels and metadata.


Extracted objects, along with entire frames and crops, are indexed using deep features. Feature vectors are used for visual search retrieval.


Deploy on variety of machines with/without GPUs, local & cloud. Docker compose enables automated setup of Postgres & RabbitMQ.

Features & Models

We take significant efforts to ensure that following models (code+weights included) work without having to write any code.


  • Visual Search as a primary interface

  • Upload videos, multiple images.

  • Provide Youtube url to be automatically downloaded.

  • Pre-trained recognition/detection, face recognition models.

  • Train custom detector models (more type of models coming soon!)

  • Metadata stored in Postgres, all operations performed asynchronously.

  • Celery allows video & query flows to be easily modified.

  • Videos, frames, indexes, etc. stored in media directory, served through nginx.

  • Perform full-text search on text metadata and names.

  • Manually run code & tasks without UI using a Jupyter notebook.

  • Customize by specifying environment variables


Train custom models (New!)

  • Train custom YOLO detection models using region annotations

  • More functionality for training and visualization coming soon!

Import external datasets using VDN


  • Labeled Faces in the Wild


Coming Soon!

Deep Video Analytics + Visual Data Network

Deep Video Analytics


Visual Data Network

Seamless integration with Visual Data Network

Quickly import pre-processed datasets

Data & processing model


Pre-built docker images for both CPU & GPU versions are available on Docker Hub.

Machines without an Nvidia GPU

Deep Video analytics is implemented using Docker and works on Mac, Windows and Linux. Make sure you have latest version of Docker installed.

git clone
cd DeepVideoAnalytics/docker && docker-compose up

Machines with Nvidia GPU

You need to have latest version of Docker and nvidia-docker installed. The GPU Dockerfile is slightly different from the CPU version dockerfile.

pip install --upgrade nvidia-docker-compose
git clone
cd DeepVideoAnalytics/docker && ./
nvidia-docker-compose -f custom_compose/docker-compose-gpu.yml up

Security warning

When deploying/running on remote Ubuntu machines on VPS services such as Linode etc. beware of the Docker/UFW firewall issues. Docker bypasses UFW firewall and opens the port 8000 to internet. You can change the behavior by using a loopback interface ( and then forwarding the port (8000) over SSH tunnel, an example of this is shown here.

Architecture & Deployment

Deep Video Analytics can be deployed on cloud in a scalable cost-effective manner to effectively leverage spot-pricing, cheap storage without any significant changes to codebase. This website and associated applications are deployed using this method.

Documentation & Presentation

Some documentation is available here along with a board for planned future tasks.

For a quick overview of design choices and vision behind this project we strongly recommend going through following presentation.

Paper & Citation

Coming Soon!


  1. Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

  2. Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

  3. Zhang, Kaipeng, et al. "Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks." IEEE Signal Processing Letters 23.10 (2016): 1499-1503.

  4. Liu, Wei, et al. "SSD: Single shot multibox detector." European Conference on Computer Vision. Springer International Publishing, 2016.

  5. Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.

  6. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.

  7. Johnson, Jeff, Matthijs Douze, and Hervé Jégou. "Billion-scale similarity search with GPUs." arXiv preprint arXiv:1702.08734 (2017).

Issues, Questions & Contact

Please submit all software related bugs and questions using Github issues, for other questions you can contact me at

© 2017 Akshay Bhat, Cornell University.
All rights reserved.