SegNet-Tutorial | train SegNet for road scenes | Machine Learning library

by alexgkendall Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | SegNet-Tutorial Summary

SegNet-Tutorial is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. SegNet-Tutorial has no bugs, it has no vulnerabilities and it has low support. However SegNet-Tutorial build file is not available. You can download it from GitHub.

This repository contains all the files for you to complete the 'Getting Started with SegNet' and the 'Bayesian SegNet' tutorials here: Please see this link for detailed instructions.

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SegNet-Tutorial has a low active ecosystem.

It has 724 star(s) with 495 fork(s). There are 42 watchers for this library.

It had no major release in the last 6 months.

There are 100 open issues and 58 have been closed. On average issues are closed in 37 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of SegNet-Tutorial is current.

Quality

SegNet-Tutorial has no bugs reported.

Security

SegNet-Tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

SegNet-Tutorial does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

SegNet-Tutorial releases are not available. You will need to build from source code and install.

SegNet-Tutorial has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed SegNet-Tutorial and discovered the below as its top functions. This is intended to give you an instant insight into SegNet-Tutorial implemented functionality, and help decide if they suit your requirements.

Create test files .
Batch absorption of weights
Create a testable .
Create a protobuf .
Create an argument parser .
Extracts the dataset from a net message .
Copy double array data .

Get all kandi verified functions for this library.

SegNet-Tutorial Key Features

No Key Features are available at this moment for SegNet-Tutorial.

SegNet-Tutorial Examples and Code Snippets

No Code Snippets are available at this moment for SegNet-Tutorial.

Community Discussions

Trending Discussions on SegNet-Tutorial

caffe forward net in a for loop not working

How many bottoms/tops a layer can have in caffe structure

QUESTION

caffe forward net in a for loop not working

Asked 2018-Sep-27 at 08:41

I am currently trying to write a c++ wrapper for PSPNet's prediction (originally in Matlab). PSPNet runs on Caffe.

Situation: I have a trained caffe model, and would like to implement this wrapper to run the segmentation result when given an input. In this case, my crop_size is smaller than it's original size. Thus, it is being cropped manually to multiple 425x425 "frames" and fed forward into caffe net after the pre-processes in a for-loop.

Problem: However, net seems to only be running forward once despite being in a for loop. Supported by its processing time and output, refer below.

This is the incomplete code I am currently trying to work on:

...

ANSWER

Answered 2018-Sep-27 at 08:41

This issue is solved by wrapping the input channel each time it is changed so that the input will be fed forward correctly.

Thus the function:

Source https://stackoverflow.com/questions/52489996

QUESTION

How many bottoms/tops a layer can have in caffe structure

Asked 2017-May-10 at 08:14

i keep seeing some layers in caffe that has two bottoms(source) or tops(destination) in Neural networks architecture,for example this is Segnet data layer that has two tops ,data and label given from the same source file on the same line img1.png lagel1.png

...

ANSWER

Answered 2017-May-10 at 08:14

Think of a layer as a mathematical operation: each layer type performs different operation. "Convolution" layer convolves the input with the layer's internal parameters, "ReLU" performs linear rectification etc.
Some operations do not require any inputs ("bottom"s): these are usually input layers that brings data/labels into the net.
Other layers only act on a single operand (one "bottom") and outputs a single result (one "top"): "Convolution", "ReLU", "Softmax" just to name a few.
Other layers may produce several outputs (many "top"s), e.g., "Slice" layer.
And you can also find layers that takes several inputs and produce a single output, e.g., "Eltwise" layer.

Bottom line, each layer/operation requires a different number of inputs and may produce a different number of outputs. You should not confuse between input/output blobs and the layer's operation.

For more information about caffe's layers you can find at caffe.help.

Source https://stackoverflow.com/questions/43886064

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install SegNet-Tutorial

If you would just like to try out an example model, then you can find the model used in the SegNet webdemo in the folder Example_Models/. You will need to download the weights separately using the link in the SegNet Model Zoo.
Use docker to compile caffe and run the examples. In order to run caffe on the gpu using docker, please install nvidia-docker (see https://github.com/NVIDIA/nvidia-docker or using ansbile: https://galaxy.ansible.com/ryanolson/nvidia-docker/).

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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