Third Party Deep Neural Networks Model Services
A collection of services using third party DNN models.
Getting Started
For more details on how to publish and test a service, select it from the list below:
Audio:
- real-time-voice-cloning (User's Guide) - This service uses Real-Time-Voice-Cloning to clone a voice from a 5 seconds audio file to generate arbitrary speech in real-time. [Reference]
- sound-spleeter (User's Guide) - This service uses Deezer's Spleeter to perform source separation on audio files. [Reference]
@misc{spleeter2019, title={Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models}, author={Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam}, howpublished={Late-Breaking/Demo ISMIR 2019}, month={November}, note={Deezer Research}, year={2019} }
Images:
- cntk-image-recon (User's Guide) - This service uses ResNet152 model, trained to recognize different types of flowers and dog breeds. [Reference]
- deepfakes-faceswap (User's Guide) - This service uses the Deepfakes Faceswap, trained on two input videos A and B, to perform face swapping on videos. [Reference]
- deoldify-colorizer (User's Guide) - This service colorizes grayscale images using an AI model. [Reference]
- yolov3-object-detection (User's Guide) - This service uses YOLOv3 model to detect objects on images. [Reference]
@article{yolov3, title={YOLOv3: An Incremental Improvement}, author={Redmon, Joseph and Farhadi, Ali}, journal = {arXiv}, year={2018} } - places365-scene-recognition (User's Guide) - This service uses various convolutional neural networks trained on Places365 to perform scene recognition. [Reference]
@article{zhou2017places, title={Places: A 10 million Image Database for Scene Recognition}, author={Zhou, Bolei and Lapedriza, Agata and Khosla, Aditya and Oliva, Aude and Torralba, Antonio}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2017}, publisher={IEEE} } - pneumonia-diagnosis (User's Guide) - This service uses VGG19 to classify X-ray chest images. Based on Alishba Imran's work.
Videos:
- i3d-video-action-recognition (User's Guide) - This service uses I3D model to recognize actions on videos (with 400 or 600 labels). [Reference]
- s2vt-video-captioning (User's Guide) - This service uses "Sequence to Sequence - Video to Text" to describe video content with natural language text. [Reference]
@inproceedings{venugopalan15iccv, title = {Sequence to Sequence -- Video to Text}, author = {Venugopalan, Subhashini and Rohrbach, Marcus and Donahue, Jeff and Mooney, Raymond and Darrell, Trevor and Saenko, Kate}, booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)}, year = {2015} }
Games:
- zeta36-chess-alpha-zero (User's Guide) - This service uses AlphaGo Zero methods to learn and play chess. [Reference]
Contributing and Reporting Issues
Please read our guidelines before submitting an issue. If your issue is a bug, please use the bug template pre-populated here. For feature requests and queries you can use this template.
Authors
- Artur Gontijo - Maintainer - SingularityNET
- Ramon Durães - Maintainer - SingularityNET
Licenses
This project is licensed under the MIT License - see the LICENSE file for details.
Each service is licensed as followed:
- cntk-image-recon - MIT License
- deepfakes-faceswap - GPL-3.0
- deoldify-colorizer - MIT License
- i3d-video-action-recognition - Apache License 2.0
- places365-scene-recognition - MIT License
- pneumonia-diagnosis - Attribution 4.0 International (CC BY 4.0)
- real-time-voice-cloning - MIT License
- s2vt-video-captioning - Attribution 4.0 International (CC BY 4.0)
- sound-spleeter - MIT License
- yolov3-object-detection - Public domain
- zeta36-chess-alpha-zero - MIT License
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent