MODNet | Free Portrait Matting Solution in Real Time

by ZHKKKe Python Version: Current License: Apache-2.0

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

kandi X-RAY | MODNet Summary

MODNet is a Python library. MODNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However MODNet build file is not available. You can download it from GitHub.

Online Solution (在线方案) | Research Demo | Arxiv Preprint | Supplementary Video. Community | Code | PPM Benchmark | License | Acknowledgement | Citation | Contact. News: We create a repository for our new model MODNet-V that focuses on faster and better portrait video matting. News: The PPM-100 benchmark is released in this repository.

Support

Quality

Security

License

Reuse

Support

MODNet has a medium active ecosystem.

It has 3189 star(s) with 560 fork(s). There are 102 watchers for this library.

It had no major release in the last 6 months.

There are 54 open issues and 143 have been closed. On average issues are closed in 102 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of MODNet is current.

Quality

MODNet has 0 bugs and 0 code smells.

Security

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

MODNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

MODNet is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

MODNet saves you 272 person hours of effort in developing the same functionality from scratch.

It has 1115 lines of code, 73 functions and 17 files.

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

Top functions reviewed by kandi - BETA

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

Implementation of soc_adaptation .
Perform supervised training .
Takes a video .
Initialize the convolutional layer .
Initialize weights .
load pre - trained model
Divide a value into a divisible value .
Freeze the norm layers .
Create a convolutional Conv2d .
convolution 2x1 layer .

Get all kandi verified functions for this library.

MODNet Key Features

No Key Features are available at this moment for MODNet.

MODNet Examples and Code Snippets

No Code Snippets are available at this moment for MODNet.

Community Discussions

Trending Discussions on MODNet

Crash when trying to export PyTorch model to ONNX: forward() missing 1 required positional argument

QUESTION

Crash when trying to export PyTorch model to ONNX: forward() missing 1 required positional argument

Asked 2021-Feb-18 at 16:09

I'm trying to convert pyTorch model to onnx like this:

...

ANSWER

Answered 2021-Feb-18 at 16:09

Modnet forward method requires a parameter called inference which is a boolean, indeed when the model is trained they pass it in this way:

Source https://stackoverflow.com/questions/66263259

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

Vulnerabilities

No vulnerabilities reported

Install MODNet

You can download it from GitHub.
You can use MODNet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.