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.
- 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.
- Motivation
- User Guide
- Installation and Administration Guide
- Developer Guide
- Thorough Description and Context in PhD Thesis Scaling Wearable Cognitive Assistance (Section 6.3)
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}
}
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.