PANet | PANet for Instance Segmentation and Object Detection | Computer Vision library

by ShuLiu1993 Python Version: Current License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | PANet Summary

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

This repository is for the CVPR 2018 Spotlight paper, 'Path Aggregation Network for Instance Segmentation', which ranked 1st place of COCO Instance Segmentation Challenge 2017 , 2nd place of COCO Detection Challenge 2017 (Team Name: UCenter) and 1st place of 2018 Scene Understanding Challenge for Autonomous Navigation in Unstructured Environments (Team Name: TUTU).

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Quality

Security

License

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Support

PANet has a medium active ecosystem.

It has 1275 star(s) with 280 fork(s). There are 28 watchers for this library.

It had no major release in the last 6 months.

There are 52 open issues and 13 have been closed. On average issues are closed in 52 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PANet is current.

Quality

PANet has 0 bugs and 0 code smells.

Security

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

PANet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PANet 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

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

PANet 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.

Top functions reviewed by kandi - BETA

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

Visualize one image
Convert boxes in cls format
Get class string
Return a colormap
Forward prediction
Filters boxes based on given size
Generate proposals for one image
R Evaluate the exposure of a box
Return a dict of box_proposal results
Helper function for parallel_apply
Recursively update iteration stats
Perform the forward computation
Forward RNN to RNN
Sample a two grid
Assert that the weights file is loaded
Convert an image into a blob
Map detectron weights to detectron weight
Download and cache a given URL
Generate a field of anchor points
Check that the expected results are in the expected set
Convert heatmaps to keypoints
Process an image in parallel
Parse command line arguments
Load pretrained image weights
Convert city - scapes instance
Create a combined ROIDB for training data

Get all kandi verified functions for this library.

PANet Key Features

No Key Features are available at this moment for PANet.

PANet Examples and Code Snippets

预测步骤,b、使用自己训练的权重

Python

Lines of Code : 43

License : Permissive (MIT)

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_defaults = {
    #--------------------------------------------------------------------------#
    #   使用自己训练好的模型进行预测一定要修改model_path和classes_path！
    #   model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
    #   如果出现shape不匹配，同时要注意训练时的model_pat

预测步骤,b、使用自己训练的权重

Python

Lines of Code : 42

License : Permissive (MIT)

Copy

_defaults = {
    #--------------------------------------------------------------------------#
    #   使用自己训练好的模型进行预测一定要修改model_path和classes_path！
    #   model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
    #   如果出现shape不匹配，同时要注意训练时的model_pat

预测步骤,b、使用自己训练的权重

Python

Lines of Code : 14

License : Permissive (MIT)

Copy

_defaults = {
    "model_path"        : 'model_data/yolov4_mobilenet_v1_voc.h5',
    "anchors_path"      : 'model_data/yolo_anchors.txt',
    "classes_path"      : 'model_data/voc_classes.txt',
    "backbone"          : 'mobilenetv1',
    "alpha"

Community Discussions

Trending Discussions on PANet

Unable to understand YOLOv4 architecture

QUESTION

Unable to understand YOLOv4 architecture

Asked 2021-Jan-31 at 05:53

I was going through yolov4 paper where the authors have mentioned Backbone(CSP DARKNET-53), Neck (SPP followed by PANet) & than Head(YOLOv3). Hence is the architecture something like this:

CSP Darknet-53-->SPP-->PANet-->YOLOv3(106 layers of YOLOv3).

Does this mean YOLOv4 incorporates entire YOLOv3?

...

ANSWER

Answered 2021-Jan-31 at 05:53

First, what is YOLOv3 composed of?

YOLOv3 is composed of two parts:

Backbone or Feature Extractor --> Darknet53
Head or Detection Blocks --> 53 layers

The head is used for (1) bounding box localization, and (2) identify the class of the object inside the box.

In the case of YOLOv4, it uses the same "Head" with that of YOLOv3.

To summarize, YOLOv4 has three main parts:

Backbone --> CSPDarknet53
Neck (Connects the backbone with the head) --> SPP, PAN
Head --> YOLOv3's Head

References:

Section 1.A. in https://ieeexplore.ieee.org/document/9214094
Page 5 of http://arxiv.org/abs/2004.10934

Source https://stackoverflow.com/questions/65971973

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

Vulnerabilities

No vulnerabilities reported

Install PANet

For environment requirements, data preparation and compilation, please refer to Detectron.pytorch. WARNING: pytorch 0.4.1 is broken, see https://github.com/pytorch/pytorch/issues/8483. Use pytorch 0.4.0.

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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