COVNet | Artificial Intelligence Distinguishes COVID-19 | Machine Learning library

by bkong999 Python Version: Current License: Non-SPDX

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

kandi X-RAY | COVNet Summary

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

This is a PyTorch implementation of the paper "Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT". It supports training, validation and testing for COVNet.

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Quality

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COVNet has a low active ecosystem.

It has 160 star(s) with 61 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 24 have been closed. On average issues are closed in 15 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of COVNet is current.

Quality

COVNet has 0 bugs and 0 code smells.

Security

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

COVNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

COVNet has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

COVNet 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.

COVNet saves you 155 person hours of effort in developing the same functionality from scratch.

It has 386 lines of code, 17 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Evaluate the model
Prints progress bar for training
Calculates the accuracy for each class
Compute the classifier
Train the model
Print training progress
Prints the epoch progress
Parse command line arguments
Generate weights for balanced classification
Get the learning rate of an optimizer

Get all kandi verified functions for this library.

COVNet Key Features

No Key Features are available at this moment for COVNet.

COVNet Examples and Code Snippets

No Code Snippets are available at this moment for COVNet.

Community Discussions

Trending Discussions on COVNet

Correct way to apply gradients in TF2 custom training loop with multiple Keras models

QUESTION

Correct way to apply gradients in TF2 custom training loop with multiple Keras models

Asked 2020-Apr-10 at 07:37

I am working to implement a custom training loop with GradientTape involving multiple Keras models. I have 3 networks, model_a, model_b, and model_c. I have created a list to hold their trainbale_weights as:

...

ANSWER

Answered 2020-Apr-10 at 07:37

The problem is that tape.gradient expects trainables to be a flat list of trainable variables rather than a list of lists. You can solve this issue by concatenating all the trainable weights into a flat list:

Source https://stackoverflow.com/questions/60395398

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

Vulnerabilities

No vulnerabilities reported

Install COVNet

You can download it from GitHub.
You can use COVNet 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.

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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