Random-Erasing | Random Erasing Data Augmentation Experiments on CIFAR10, CIFAR100 and Fashion-MNIST | Machine Learning library

by zhunzhong07 Python Version: Current License: Apache-2.0

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

kandi X-RAY | Random-Erasing Summary

Random-Erasing is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. Random-Erasing has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Random-Erasing build file is not available. You can download it from GitHub.

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Quality

Security

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Random-Erasing has a low active ecosystem.

It has 660 star(s) with 144 fork(s). There are 14 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 9 have been closed. On average issues are closed in 24 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Random-Erasing is current.

Quality

Random-Erasing has 0 bugs and 0 code smells.

Security

Random-Erasing has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Random-Erasing code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Random-Erasing is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Random-Erasing 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.

Random-Erasing saves you 638 person hours of effort in developing the same functionality from scratch.

It has 1482 lines of code, 105 functions and 23 files.

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

Top functions reviewed by kandi - BETA

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

Show the masks
Make an image
Show a single image
Colorize an image
Create a new layer
Append numbers to the file
Write string to file
Update progress bar
Write a line to the file
Compute the accuracy of a target
Sets the list of names
Plot the test error
Plots the overlap plot
Create a layer
Show a batch of images
Update the progress

Get all kandi verified functions for this library.

Random-Erasing Key Features

No Key Features are available at this moment for Random-Erasing.

Random-Erasing Examples and Code Snippets

No Code Snippets are available at this moment for Random-Erasing.

Community Discussions

Trending Discussions on Random-Erasing

How to skip the current iteration of tf.while_loop()?

QUESTION

How to skip the current iteration of tf.while_loop()?

Asked 2020-Jun-16 at 11:37

I have only recently started working with Tensorflow2. I'm trying to re-program a script that randomly cuts squares out of images. The original code comes from this github repository: Link. I fail due to the tf.while_for() loop in Tensorflow2. But here is the code I wrote so far:

...

ANSWER

Answered 2020-Jun-16 at 11:37

There are several issues in the code, you shouldn't use tf.Variable objects for this, those tf.map_fn are avoidable and tf.cond must always have two branches. Here is a possible implementation of the code you linked in TensorFlow, adapted to work on batches of images. Each image in the batch is independently modified with the given probability on a different box. I broken down the logic in several functions for clarity.

Source https://stackoverflow.com/questions/62405567

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

Vulnerabilities

No vulnerabilities reported

Install Random-Erasing

Requirements for Pytorch （see [Pytorch](http://pytorch.org/) installation instructions）.

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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