Open-Source Toolkit for Painless Object Detection.

Training a DNN for custom object detection is not trivial. In particular, it involves constructing a correctly-labeled training data set with millions of positive and negative examples. The training process itself may take days to complete, and requires a set of arcane procedures to ensure both convergence and efficacy of the model. Fortunately, one does not typically need to train a DNN from scratch. Rather, pretrained models based on public image data sets such as ImageNet are publicly available. Developers can adapt these pretrained models to detect custom classes for new applications, through a process called transfer learning. The key assumption of transfer learning is that much of the training that teaches the model to discover low-level features, such as edges, textures, shapes, and patterns that are useful in distinguishing objects can be reused. Thus, adapting a pretrained model for new object classes requires only thousands or tens of thousands of examples and hours of training time. However, even with transfer learning, collecting a labeled training set of several thousand examples per object class can be a daunting and painful task. In addition, implementing object detection DNNs itself requires expertise and takes time.

OpenTPOD is a web-based tool to streamline the process of creating DNN-based object detectors for fast prototyping. It provides an assistive labeling interface for speedy annotation and a DNN training and evaluation portal that leverages transfer learning to hide the nuances of DNN creation. It greatly reduces the labeling effort while constructing a dataset, and automates training an object detection DNN model.

https://www.youtube.com/watch?v=B_PX5SSSLJM

See docs/server-guide.md.