https://github.com/cmusatyalab/opentpod

Open Toolkit for Painless Object Detection
https://github.com/cmusatyalab/opentpod

automl custom-vision custom-vision-api deep-learning deep-neural-network deep-neural-networks django django-rest-framework dnn fine-tuning object-detection object-detection-api object-detection-model object-detection-pipelines object-detection-tutorial reactjs tensorflow tensorflow-models tensorflow-serving transfer-learning

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Open Toolkit for Painless Object Detection

• Host: GitHub
• URL: https://github.com/cmusatyalab/opentpod
• Owner: cmusatyalab
• License: apache-2.0
• Created: 2019-07-17T17:25:13.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-11-21T21:09:19.000Z (almost 2 years ago)
• Last Synced: 2024-09-23T08:03:08.999Z (10 days ago)
• Topics: automl, custom-vision, custom-vision-api, deep-learning, deep-neural-network, deep-neural-networks, django, django-rest-framework, dnn, fine-tuning, object-detection, object-detection-api, object-detection-model, object-detection-pipelines, object-detection-tutorial, reactjs, tensorflow, tensorflow-models, tensorflow-serving, transfer-learning
• Language: CSS
• Homepage: https://youtu.be/UHnNLrD6jTo
• Size: 46.4 MB
• Stars: 119
• Watchers: 5
• Forks: 19
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# OpenTPOD

*Create deep learning based object detectors without writing a single line of code.*

OpenTPOD is an all-in-one open-source tool for nonexperts to create custom deep

neural network object detectors. It is designed to lower the barrier of entry

and facilitates the end-to-end authoring workflow of custom object detection

using state-of-art deep learning methods.

It provides the following features via an easy-to-use web interface.

* Training data management.

* Data annotation through seamless integration with [OpenCV CVAT Labeling Tool](https://github.com/opencv/cvat).

* One-click training/fine-tuning of object detection deep neural networks,

  including SSD MobileNet, Faster RCNN Inception, and Faster RCNN ResNet, using

  Tensorflow (with and without GPU).

* One-click model export for inference with Tensorflow Serving.

* Extensible architecture for easy addition of new deep neural network architectures.

## Demo Video

[![OpenTPOD Demo Video](http://img.youtube.com/vi/UHnNLrD6jTo/0.jpg)](https://youtu.be/UHnNLrD6jTo)

## Documentation

* [Motivation](docs/motivation.md)

* [User Guide](docs/user-guide.md)

* [Installation and Administration Guide](docs/server-guide.md)

* [Developer Guide](docs/notes.md)

* [Thorough Description and Context in PhD Thesis *Scaling Wearable Cognitive Assistance* (Section 6.3)](https://junjuew.github.io/assets/thesis.pdf)

## Citations

Please cite the following thesis if you find OpenTPOD helps your research.

```

@phdthesis{wang2020scaling,

  title={Scaling Wearable Cognitive Assistance},

  author={Wang, Junjue},

  year={2020},

  school={CMU-CS-20-107, CMU School of Computer Science}

}

```

## Acknowledgement

This research was supported by the National Science Foundation (NSF) under grant

number CNS-1518865. Additional support was provided by Intel, Vodafone, Deutsche

Telekom, Verizon, Crown Castle, Seagate, VMware, MobiledgeX, InterDigital, and

the Conklin Kistler family fund.