SemanticSegmentation | training segmentation models in pytorch on labelme | Computer Vision library

by WillBrennan Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | SemanticSegmentation Summary

SemanticSegmentation is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. SemanticSegmentation has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However SemanticSegmentation build file is not available. You can download it from GitHub.

This project started as a replacement to the Skin Detection project that used traditional computer vision techniques. This project implements two models,. These models are trained with masks from labelme annotations. As labelme annotations allow for multiple categories per a pixel we use multi-label semantic segmentation. Both the accurate and real-time models are in the pretrained directory.

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SemanticSegmentation has a low active ecosystem.

It has 64 star(s) with 9 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 18 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SemanticSegmentation is current.

Quality

SemanticSegmentation has 0 bugs and 0 code smells.

Security

SemanticSegmentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SemanticSegmentation code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SemanticSegmentation is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

SemanticSegmentation releases are not available. You will need to build from source code and install.

SemanticSegmentation 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.

SemanticSegmentation saves you 362 person hours of effort in developing the same functionality from scratch.

It has 869 lines of code, 71 functions and 16 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Save labelme data to image directory
Convert an annotation to a polygon shape
Given a list of images and a set of categories return a mapping of images to images
Attach a training logger to the writer
Create data loaders
Attach metrics to a trainer
Parse command line arguments
Attach model checkpoint handler
Attaches lrscheduler to the writer
Load image from file
Load a model from a dict
Generate a function that takes a threshold
Logs statistics

Get all kandi verified functions for this library.

SemanticSegmentation Key Features

No Key Features are available at this moment for SemanticSegmentation.

SemanticSegmentation Examples and Code Snippets

No Code Snippets are available at this moment for SemanticSegmentation.

Community Discussions

Trending Discussions on SemanticSegmentation

Unittest the pytorch forward function

QUESTION

Unittest the pytorch forward function

Asked 2020-Sep-16 at 15:46

I want to unittest the overridden forward function of my Network modell in Pytorch. So I loaded my model (pretrained from Zoo) with the setUp method, loaded a seed and created some random batch. In my method testForward I tested the result of forward against shape and numel, but I also want to check a specific value which a apears to be 0. I wasn't shure about that so checked my params in setUp also, which appears not to be 0.

...

ANSWER

Answered 2020-Sep-16 at 15:46

Ok after tinkering around and debugging the forward function I came to following explanation:

Some Information about the architecture

If you do classes from Andrew Ng or others you learn not to initialize the weights to the same value, as example "0". This is what the writers of the original Paper of FCNs do and they say, because it doesn't change the performance or didn't yield to faster convergence (FCN-Paper).

My Solution

So for testing purpose I initlize in the testing module to seeded random values, which I can test against:

Source https://stackoverflow.com/questions/63917991

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

Vulnerabilities

No vulnerabilities reported

Install SemanticSegmentation

The pretrained models are stored in the repo with git-lfs, when you clone make sure you've pulled the files by calling,. or by downloading them from github directly. This project uses conda to manage its enviroment; once conda is installed we create the enviroment and activate it,. . On windows; powershell needs to be initialised and the execution policy needs to be modified.

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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