ttach | Image Test Time Augmentation with PyTorch | Machine Learning library

by qubvel Python Version: 0.0.3 License: MIT

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kandi X-RAY | ttach Summary

ttach is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. ttach has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install ttach' or download it from GitHub, PyPI.

Image Test Time Augmentation with PyTorch!. Similar to what Data Augmentation is doing to the training set, the purpose of Test Time Augmentation is to perform random modifications to the test images. Thus, instead of showing the regular, “clean” images, only once to the trained model, we will show it the augmented images several times. We will then average the predictions of each corresponding image and take that as our final guess [1].

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Quality

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Support

ttach has a medium active ecosystem.

It has 853 star(s) with 55 fork(s). There are 10 watchers for this library.

It had no major release in the last 12 months.

There are 11 open issues and 5 have been closed. On average issues are closed in 4 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ttach is 0.0.3

Quality

ttach has 0 bugs and 0 code smells.

Security

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

ttach code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ttach 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

ttach releases are available to install and integrate.

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Compute the next transformer
Append a value to the output
Return the maximum of two arrays
Return the minimum of two arrays
Run Twine
Print a status message
Returns the result of applying merges
Remove the given mask from the pipeline
Apply deaug
Resize image
Scale the image
Scale image
Removes keypoints from keypoints
Disassemble keypoints
Flip keypoints
Assemble keypoints
Augment image
Multiply x by factor
Rotate keypoints
Rotate key points
Transform keypoints
Apply an image to the image
Transform an image
Rotate x along axis

Get all kandi verified functions for this library.

ttach Key Features

No Key Features are available at this moment for ttach.

ttach Examples and Code Snippets

No Code Snippets are available at this moment for ttach.

Community Discussions

Trending Discussions on ttach

dependency parsing (bracket format) - spanish - using nltk and stanford-nlp tag

QUESTION

dependency parsing (bracket format) - spanish - using nltk and stanford-nlp tag

Asked 2018-Nov-08 at 18:36

I am trying to parse a plain text corpus of Spanish to get a result like SNLI corpus (used for entailment), I´ve ttached an extract of snli corpus below.

The church has cracks in the ceiling. ( ( The church ) ( ( has ( cracks ( in ( the ceiling ) ) ) ) . ) ) (ROOT (S (NP (DT The) (NN church)) (VP (VBZ has) (NP (NP (NNS cracks)) (PP (IN in) (NP (DT the) (NN ceiling))))) (. .)))

I tried the following code but the output was not good at all.

...

ANSWER

Answered 2018-Nov-08 at 15:15

Thank you for your kind feedback. I tried your way for getting the output but it´s using UD tagsets and does nothing:

Source https://stackoverflow.com/questions/53047808

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

Vulnerabilities

No vulnerabilities reported

Install ttach

Note: the model must return keypoints in the format torch([x1, y1, ..., xn, yn]).

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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