Yolact_minimal | Minimal PyTorch implementation of YOLACT | Machine Learning library

by feiyuhuahuo Python Version: Current License: No License

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kandi X-RAY | Yolact_minimal Summary

Yolact_minimal is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. Yolact_minimal has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Minimal PyTorch implementation of Yolact:《YOLACT: Real-time Instance Segmentation》. The original project is here. This implementation simplified the original code, preserved the main function and made the network easy to understand. This implementation has not been updated to Yolact++.

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Yolact_minimal has a low active ecosystem.

It has 217 star(s) with 69 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 61 have been closed. On average issues are closed in 31 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Yolact_minimal is current.

Quality

Yolact_minimal has 0 bugs and 0 code smells.

Security

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

Yolact_minimal code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Yolact_minimal 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

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

Build file is available. You can build the component from source.

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

Yolact_minimal saves you 798 person hours of effort in developing the same functionality from scratch.

It has 2561 lines of code, 124 functions and 21 files.

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

Top functions reviewed by kandi - BETA

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

Evaluate an image
Dump data to disk
Add a bounding box
Add a mask to the layer
Compute NMS
Compute the loss function
Compute the L1 correlation matrix
Compute the loss
After numpy ndarray
Sanitize coordinates
Crop a numpy array
Perform a forward transformation on a feature matrix
Partitions x into a list of windows
Reverse a window of windows
Wrapper for nms
Compute the NMS for the given boxes
Get configuration
Print the cfg configuration
Encodes the given matrices
Augment an image
Saves the latest weight
Compute the NMS score
Saves the best model in the network
Load weights
Generate anchors for the image
Forward attention layer
Draw an image
Return the bounding box of a given mask

Get all kandi verified functions for this library.

Yolact_minimal Key Features

No Key Features are available at this moment for Yolact_minimal.

Yolact_minimal Examples and Code Snippets

No Code Snippets are available at this moment for Yolact_minimal.

Community Discussions

Trending Discussions on Yolact_minimal

Encoded and decoded version of bouding box regression offsets are different

QUESTION

Encoded and decoded version of bouding box regression offsets are different

Asked 2020-Aug-21 at 08:23

I'm trying to replicate bounding box regression technique used in faster-rcnn as given here. I've made a decoding fuunction and an encoding function. Ideally, when passing a bounding box to the encoder and then decoding it, I should get the same bounding box.

Here, are my input bounding boxes:

...

ANSWER

Answered 2020-Aug-21 at 08:23

The problem was in my decode function in calculating [x_min, y_min, x_max, y_max]. It should have been like this:

Source https://stackoverflow.com/questions/63434199

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

Vulnerabilities

No vulnerabilities reported

Install Yolact_minimal

You can download it from GitHub.
You can use Yolact_minimal 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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