CSRNet-pytorch | The simple implemention of CSRNet

by CommissarMa Python Version: Current License: MIT

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

kandi X-RAY | CSRNet-pytorch Summary

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

The simple implemention of CSRNet.

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CSRNet-pytorch has a low active ecosystem.

It has 35 star(s) with 6 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CSRNet-pytorch is current.

Quality

CSRNet-pytorch has 0 bugs and 0 code smells.

Security

CSRNet-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CSRNet-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CSRNet-pytorch 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

CSRNet-pytorch releases are not available. You will need to build from source code and install.

CSRNet-pytorch 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.

CSRNet-pytorch saves you 155 person hours of effort in developing the same functionality from scratch.

It has 387 lines of code, 22 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Generate k nearest truth kernel density map .
Generate a fixed kernel density map .
Generate a ground truth density map .
Create data loader .
Make a sequence of layers .
Initialize the layers .
Create a test dataset .
Randomly map the image .
Initialize weights .
create directory

Get all kandi verified functions for this library.

CSRNet-pytorch Key Features

No Key Features are available at this moment for CSRNet-pytorch.

CSRNet-pytorch Examples and Code Snippets

No Code Snippets are available at this moment for CSRNet-pytorch.

Community Discussions

Trending Discussions on CSRNet-pytorch

I want to train the CSRNet model on UCF_CC_50 dataset which has total number of images 50. and face a problem like

QUESTION

I want to train the CSRNet model on UCF_CC_50 dataset which has total number of images 50. and face a problem like

Asked 2021-Jun-05 at 04:47

I want to train the CSRNet model on UCF_CC_50 dataset but occurring this problem

...

ANSWER

Answered 2021-Jun-03 at 13:08

You are reading a matfile '...\ground_truth\GT_IMG_1.mat' corresponding to the image '...\IMG_1.jpg'. While you process this data point, you try to access variable 'image_info' stored in the matfile you read. As the error message you got states:

KeyError: 'image_info'

The matfile does not contain this variable, 'image_info'.

to debug, read the matfile and see what are the names of the variables stored there. Notice that naming them is case sensitive.

Source https://stackoverflow.com/questions/67815809

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

Vulnerabilities

No vulnerabilities reported

Install CSRNet-pytorch

Download ShanghaiTech Dataset from Dropbox or Baidu Disk (code: a2v8)
Put ShanghaiTech Dataset in 'ROOT/data'. You can find two python scripts in 'data_preparation' folder which are used to generate ground truth density-map for ShanghaiTech PartA and PartB respectively. (Mind that you need move the script to corresponding sub dataset folder like 'ROOT/data/part_A_final' and run it)

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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