LearningToCountEverything | official implementation of the following CVPR 2021 paper

by cvlab-stonybrook Python Version: Current License: MIT

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

kandi X-RAY | LearningToCountEverything Summary

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

This is the official implementation of the following CVPR 2021 paper:. Link to arxiv preprint:

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Quality

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License

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Support

LearningToCountEverything has a low active ecosystem.

It has 237 star(s) with 53 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 32 have been closed. On average issues are closed in 36 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of LearningToCountEverything is current.

Quality

LearningToCountEverything has no bugs reported.

Security

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

License

LearningToCountEverything 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

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

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

Visualize the input image
Format a tensor
Denormalize a tensor
Perturbation loss function
Generate a 2D Gauss - 2D Gauss - 2D
Extract features from an image
Train on FSCB
Evaluate the image
Selects images from image

Get all kandi verified functions for this library.

LearningToCountEverything Key Features

No Key Features are available at this moment for LearningToCountEverything.

LearningToCountEverything Examples and Code Snippets

No Code Snippets are available at this moment for LearningToCountEverything.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install LearningToCountEverything

Images can be downloaded from here: https://drive.google.com/file/d/1ymDYrGs9DSRicfZbSCDiOu0ikGDh5k6S/view?usp=sharing. Precomputed density maps can be found here: https://archive.org/details/FSC147-GT. Place the unzipped image directory and density map directory inside the data directory.
conda create -n fscount python=3.7 -y. python -m pip install matplotlib opencv-python notebook tqdm. conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch.

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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