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 a tutorial are being written in /tutorial directory. For a quick overview we recommend 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 |
Text Detection CTPN | MIT License |
SphereFace | MIT License |
Segment annotator | BSD 3-clause |
TF Object detection API | Apache 2.0 |
TF models/slim | Apache 2.0 |
TF models/delf | Apache 2.0 |
Youtube 8M feature extractor | 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