Gcam | use Pytorch library that makes model predictions | Data Visualization library

by Karol-G Python Version: v0.0.10 License: MIT

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

kandi X-RAY | Gcam Summary

Gcam is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Analytics, Data Visualization, Deep Learning, Pytorch applications. Gcam has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Gcam is an easy to use Pytorch library that makes model predictions more interpretable for humans. It allows the generation of attention maps with multiple methods like Guided Backpropagation, Grad-Cam, Guided Grad-Cam and Grad-Cam++. All you need to add to your project is a single line of code:.

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Support

Gcam has a low active ecosystem.

It has 26 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 59 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Gcam is v0.0.10

Quality

Gcam has 0 bugs and 0 code smells.

Security

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

Gcam code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Gcam 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

Gcam releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 4831 lines of code, 128 functions and 44 files.

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

Top functions reviewed by kandi - BETA

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

Saves the attention map
Generate Gcam2d
Generate an attention map
Resize the attention map
Generate the attention map
Compute gradient of gradients
Removes the given handlers
Auto layer selection
Compute the score of the given binary attention map
Compute the intersection over the image
Calculate the intersection over the union of the union
Preprocessing preprocessing
Inject a model into the backend
Create a new backend
Check if Gcam_dict is already injected
Dump the attention maps
Dump the scores to an Excel file
Forward computation
Forward back propagation
Interpolate data
Generate attention matrix
Compute the score for the given attention map
Returns the registered hooks
Return a list of registered hooks
Generate the transformer
Get the names of all layers in the model

Get all kandi verified functions for this library.

Gcam Key Features

No Key Features are available at this moment for Gcam.

Gcam Examples and Code Snippets

No Code Snippets are available at this moment for Gcam.

Community Discussions

Trending Discussions on Gcam

How to convert a pandas dataframe to matrix format in Python?

How to convert a dataframe with MultiIndex indices and columns that has string values to a pivot table?

How to apply filter to Python script to download specific email attachment

QUESTION

How to convert a pandas dataframe to matrix format in Python?

Asked 2021-Sep-27 at 15:19

I have a pandas dataframe df which looks as shown. (df.to_dict() is given at the end):

...

ANSWER

Answered 2021-Sep-27 at 15:15

pivot_table could help here.

The 2nd matrix can be directly obtained with:

Source https://stackoverflow.com/questions/69348450

QUESTION

How to convert a dataframe with MultiIndex indices and columns that has string values to a pivot table?

Asked 2021-Sep-15 at 06:40

I have a pandas dataframe df1 which looks as follows:

...

ANSWER

Answered 2021-Sep-15 at 06:40

You can drop level -1 from column (since you don't want it in the resulting dataframe), then stack the dataframe with dropna=False to keep the NaN values for the indices, then finally call to_frame passing the column name as value to convert the stacked series to a dataframe.

Source https://stackoverflow.com/questions/69187939

QUESTION

How to apply filter to Python script to download specific email attachment

Asked 2021-Jul-11 at 09:28

Good morning,

I am trying to add a filter to only download the csv attachment from an email, not the pdf. The below script works, but its downloading both attachments.

Thank you

...

ANSWER

Answered 2021-Jul-11 at 09:28

I haven't tried it, but this should work:

Source https://stackoverflow.com/questions/68334264

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