车辆检测,车道语义分割混合网络模型–MultiNet

正文索引 [隐藏]

MultiNet

Github
MultiNet is optimized to perform well at a real-time speed. It has two components: KittiSeg, which sets a new state-of-the art in road segmentation; and KittiBox, which improves over the baseline Faster-RCNN in both inference speed and detection performance.
The model is designed as an encoder-decoder architecture. It utilizes one VGG encoder and several independent decoders for each task. This repository contains generic code that combines several tensorflow models in one network. The code for the individual tasks is provided by the KittiSeg, KittiBox, and KittiClass repositories. These repositories are utilized as submodules in this project. This project is built to be compatible with the TensorVision back end, which allows for organizing experiments in a very clean way.
MultiNet实时速度运行良好。它有两个组成部分:Kittiseg在道路分割方面建立了一种新的技术状态;Kittibox,它在推理速度和检测性能方面比基本的Faster-RCNN要好。
该模型设计为编码器-解码器结构。它为每个任务使用一个VGG编码器和几个独立的解码器。此存储库包含在一个网络中组合多个TensorFlow模型的通用代码。单个任务的代码由kittiseg、kittibox和kitticlass存储库提供。在这个项目中,这些存储库被用作子模块。这个项目是为了与TensorVision后端兼容而构建的,它允许以清晰的方式组织实验。

Requirements需求

The code requires Tensorflow 1.0, python 2.7 as well as the following python libraries:
* matplotlib
* numpy
* Pillow
* scipy
* commentjson
Those modules can be installed using: pip install numpy scipy pillow matplotlib commentjson or pip install -r requirements.txt.
依赖于Tensorflow 1.0 亲测1.9.0以下均可,注意CUDA版本, python 2.7 以及以下依赖项:
* matplotlib
* numpy
* Pillow
* scipy
* commentjson
可使用pip install numpy scipy pillow matplotlib commentjson 或 pip install -r requirements.txt安装

Setup配置

  1. Clone this repository: git clone https://github.com/MarvinTeichmann/KittiSeg.git
  2. Initialize all submodules: git submodule update –init –recursive

克隆仓库 :it clone https://github.com/MarvinTeichmann/KittiSeg.git
初始化所有子模块:git submodule update –init –recursive

Tutorial教程

Getting started开始

Run: python demo.py --gpus 0 --input_image data/demo/demo.png to obtain a prediction using demo.png as input.
运行python demo.py --gpus 0 --input_image data/demo/demo.png 用demo.png预测
Run: python evaluate.pyto evaluate a trained model.
运行 python evaluate.py 评估模型