Guided-Zoom | paper Guided Zoom : Questioning Network Evidence

by andreazuna89 Python Version: Current License: No License

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kandi X-RAY | Guided-Zoom Summary

Guided-Zoom is a Python library typically used in Telecommunications, Media, Advertising, Marketing applications. Guided-Zoom has no bugs, it has no vulnerabilities and it has low support. However Guided-Zoom build file is not available. You can download it from GitHub.

This is a repository containing the code used in. Sarah Adel Bargal*, Andrea Zunino*, Vitali Petsiuk, Jianming Zhang, Kate Saenko, Vittorio Murino, Stan Sclaroff. "Guided Zoom: Questioning Network Evidence for Fine-grained Classification". BMVC 2019 (oral). and its journal extension. Sarah Adel Bargal*, Andrea Zunino*, Vitali Petsiuk, Jianming Zhang, Kate Saenko, Vittorio Murino, Stan Sclaroff. "Guided Zoom: Zooming into Network Evidence to Refine Fine-grained Model Decisions". IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 2021. The caffe version is the implementation code of Guided Zoom using Excitation Backprop saliency method. The pytorch version is the implementation code of Guided Zoom using GradCAM and RISE saliency methods.

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Guided-Zoom has a low active ecosystem.

It has 7 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 393 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Guided-Zoom is current.

Quality

Guided-Zoom has no bugs reported.

Security

Guided-Zoom has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Guided-Zoom 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.

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

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

Train the model
Calculate accuracy accuracy
Updates the statistics
Validate the evaluation
Generate masks
Gradient of the CAM
Compute the tag score for the given tags
Denormalize a batch
Load tags from file
Normalize a batch
Save checkpoint
Load masks from filepath

Get all kandi verified functions for this library.

Guided-Zoom Key Features

No Key Features are available at this moment for Guided-Zoom.

Guided-Zoom Examples and Code Snippets

No Code Snippets are available at this moment for Guided-Zoom.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install Guided-Zoom

The provided repository contains the code for evidence pool generation, computing, saving and combining the conventional and evidence CNN softmax predictions.
To generate the evidence pool use the code: evidence_pool_generation.py
To compute and save the softmax predicted by the conventional and evidence CNN use the code: save_softmax.py
For the final decision refinement use the code: decision_refinement.py

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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