Don't be worried by complexity of this banner, with latest version of docker installed correctly, you can run Deep Video Analytics in minutes locally (even without a GPU) using a single command.
Author: Akshay Bhat, Cornell University.
Deep Video Analytics is a platform for indexing and extracting information from videos and images. For installation instructions & demo go to https://www.deepvideoanalytics.com
Documentation along with presentation and blogpost is being written in /notebooks directory. For a quick overview we reocmmend going through the presentation in readme.pdf
| Library | Link to the license |
|---|---|
| YAD2K | MIT License |
| AdminLTE2 | MIT License |
| FabricJS | MIT License |
| Facenet | MIT License |
| JSFeat | MIT License |
| MTCNN | 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 |
| CRF as RNN | MIT License |
| SphereFace | MIT License |
| Segment annotator | BSD 3-clause |
| TF Object detection API | Apache 2.0 |
| CROW | Apache 2.0 |
| LOPQ | Apache 2.0 |
| Open Images Pre-trained network | Apache 2.0 |
- FFmpeg (not linked, called via a Subprocess)
- Tensorflow
- OpenCV
- Numpy
- Pytorch
- Docker
- Nvidia-docker
- Docker-compose
- All packages in requirements.txt & used in Dockerfiles.
Copyright 2016-2017, Akshay Bhat, Cornell University, All rights reserved.
Deep Video Analytics is currently in active development. The license will be relaxed once a stable release version is reached. Please contact me for more information. For more information see answer on this issue