ExtremeNet | Object Detection by Grouping Extreme

by xingyizhou Python Version: Current License: BSD-3-Clause

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

kandi X-RAY | ExtremeNet Summary

ExtremeNet is a Python library typically used in Pytorch applications. ExtremeNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However ExtremeNet build file is not available. You can download it from GitHub.

Code for bottom-up object detection by grouping extreme and center points:. Bottom-up Object Detection by Grouping Extreme and Center Points, Xingyi Zhou, Jiacheng Zhuo, Philipp Krähenbühl, CVPR 2019 (arXiv 1901.08043).

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Quality

Security

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Support

ExtremeNet has a medium active ecosystem.

It has 969 star(s) with 168 fork(s). There are 26 watchers for this library.

It had no major release in the last 6 months.

There are 29 open issues and 16 have been closed. On average issues are closed in 8 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ExtremeNet is current.

Quality

ExtremeNet has 0 bugs and 0 code smells.

Security

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

ExtremeNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ExtremeNet is licensed under the BSD-3-Clause License. This license is Permissive.

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

Reuse

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

ExtremeNet 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.

ExtremeNet saves you 1814 person hours of effort in developing the same functionality from scratch.

It has 4007 lines of code, 249 functions and 43 files.

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

Top functions reviewed by kandi - BETA

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

KP detection
Clips detections
Shuffle the indices
Return the path to an image file
Wrapper for _exception_decode
Left aggregation
Perform the right aggregation
Gather features
Train the model
Load pretrained model
Extracts the points from a set of points
Forward computation
Segment a heat map
Helper function for parallel_apply
Draw an arrow on the image
Filter the heatmap
Visualize class
Apply a visual mask to an image
Visit an image
Multiply a mask
Parse command line arguments
Load detections from cache file
Load detections
Execute the forward function
Visualize a bounding box
Validate the loss function
Wrapper around kp decoding
Load pretrained model from file

Get all kandi verified functions for this library.

ExtremeNet Key Features

No Key Features are available at this moment for ExtremeNet.

ExtremeNet Examples and Code Snippets

No Code Snippets are available at this moment for ExtremeNet.

Community Discussions

No Community Discussions are available at this moment for ExtremeNet.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install ExtremeNet

The code was tested with Anaconda Python 3.6 and PyTorch v0.4.1. After install Anaconda:.
Clone this repo: ExtremeNet_ROOT=/path/to/clone/ExtremeNet git clone --recursive https://github.com/xingyizhou/ExtremeNet $ExtremeNet_ROOT
Create an Anaconda environment using the provided package list from Cornernet. conda create --name CornerNet --file conda_packagelist.txt source activate CornerNet
Compiling NMS (originally from Faster R-CNN and Soft-NMS). cd $ExtremeNet_ROOT/external make

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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