2018-03: Code Available: Bonnet – Tensorflow Convolutional Semantic Segmentation Pipeline by Andres Milioto and Cyrill Stachniss

Bonnet: Tensorflow Convolutional Semantic Segmentation pipeline by Andres Milioto and Cyrill Stachniss

Bonnet is available on GitHub

Bonnet provides a framework to easily add architectures and datasets, in order to train and deploy CNNs for a robot. It contains a full training pipeline in python using Tensorflow and OpenCV, and it also some C++ apps to deploy a frozen protobuf in ROS and standalone. The C++ library is made in a way which allows to add other backends (such as TensorRT and MvNCS), but only Tensorflow and TensorRT are implemented for now. For now, we will keep it this way because we are mostly interested in deployment for the Jetson and Drive platforms, but if you have a specific need, we accept pull requests!

The networks included is based of of many other architectures (see below), but not exactly a copy of any of them. As seen in the videos, they run very fast in both GPU and CPU, and they are designed with performance in mind, at the cost of a slight accuracy loss. Feel free to use it as a model to implement your own architecture.

All scripts have been tested on the following configurations:

  • x86 Ubuntu 16.04 with an NVIDIA GeForce 940MX GPU (nvidia-384, CUDA8, CUDNN6, TF 1.4.1, TensorRT3)
  • x86 Ubuntu 16.04 with an NVIDIA GTX1080Ti GPU (nvidia-375, CUDA8, CUDNN6, TF 1.4.1, TensorRT3)
  • x86 Ubuntu 16.04 and 14.04 with no GPU (TF 1.4.1, running on CPU in NHWC mode, no TensorRT support)
  • Jetson TX2 (full Jetpack 3.2)

We also provide a Dockerfile to make it easy to run without worrying about the dependencies, which is based on the official nvidia/cuda image containing cuda9 and cudnn7.

This code is related to the following publications:

A. Milioto and C. Stachniss, “Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs”, in arXiv, 1802.08960, 2018.