SEMIT | Image translation , image generataton | Computer Vision library

by yaxingwang Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | SEMIT Summary

SEMIT is a Python library typically used in Artificial Intelligence, Computer Vision applications. SEMIT has no bugs, it has no vulnerabilities and it has low support. However SEMIT build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Image to Image translation, image generataton, few shot learning

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Quality

Security

License

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Support

SEMIT has a low active ecosystem.

It has 39 star(s) with 3 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SEMIT is current.

Quality

SEMIT has 0 bugs and 0 code smells.

Security

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

SEMIT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SEMIT has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

SEMIT has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are available. Examples and code snippets are not available.

SEMIT saves you 953 person hours of effort in developing the same functionality from scratch.

It has 2172 lines of code, 139 functions and 16 files.

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

Top functions reviewed by kandi - BETA

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

Forward pass through the co - routine
Decode adaincode to an image
Calculate the entropy loss
Assign the Gaussian parameters to the model
Resume the optimizer
Get model list
Get scheduler
Get loaders
Load images from a list of files
Calculates the real loss
Calculates the fake loss
Calculates the dis - real loss
Creates the folder and checkpoint folders
Calculates the total gradient of the model
Write loss to train writer
Save the state of the optimizer
Calculates the average loss function
Calculate the loss for the GAN
Translates the k - shot curve
Forward the transformation
Get the loaders for the given conf
Compute the translation of a k shot
Test the algorithm
Compute the inverse function
Writes an HTML table to a file
Calculate the FID for a given path

Get all kandi verified functions for this library.

SEMIT Key Features

No Key Features are available at this moment for SEMIT.

SEMIT Examples and Code Snippets

No Code Snippets are available at this moment for SEMIT.

Community Discussions

Trending Discussions on SEMIT

R- Word co-occurrence frequency within paragraph

how to delete documents in corpus that are similar

Issue in traversing inside loop in bson object

How to Structure data for Apriori Algorithm?

QUESTION

R- Word co-occurrence frequency within paragraph

Asked 2020-Jun-17 at 08:53

The dataset contains text data of 26 news articles. I would like to count word co-occurrence frequency within each paragraph, but it seems that my codes below are doing within a document (a whole article). Can you designate the level (sentence, paragraph...) for calculating co-occurrence frequency with fcm()? Or is there any other package to do so?

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ANSWER

Answered 2020-Jun-17 at 08:53

The answer is first to reshape the corpus into paragraphs, so that the new "documents" are then paragraphs from the original documents, and then compute the fcm with a "document" co-occurrence context.

Here's an example you can adapt, using the first three documents from the built-in inaugural address corpus.

Source https://stackoverflow.com/questions/62406952

QUESTION

how to delete documents in corpus that are similar

Asked 2018-May-23 at 23:29

I have a corpus of news articles on a given topic. Some of these articles are the exact same article but have been given additional headers and footers that very slightly change the content. I am trying to delete all but one of the potential duplicates so the final corpus only contains unique articles.

I decided to use cosine similiarity to identify the potential duplicates:

...

ANSWER

Answered 2018-May-23 at 23:29

In case anyone else has a similar problem, this was the solution I ended up creating:

Source https://stackoverflow.com/questions/50457753

QUESTION

Issue in traversing inside loop in bson object

Asked 2017-Apr-21 at 07:07

I have data in mongo db collection like

...

ANSWER

Answered 2017-Apr-21 at 07:07

Update your function to use distinct. This will project all the academic years for a given course id.

Source https://stackoverflow.com/questions/43534972

QUESTION

How to Structure data for Apriori Algorithm?

Asked 2017-Jan-15 at 00:55

I want to see if users who tweet about one thing also tweet about something else. I've used the TwittR package in R studio to download tweets containing keywords and then downloaded the timelines of those users in python. My data is structured as follows.

user_name,id,created_at,text

exampleuser,814495243068313603,2016-12-29 15:36:13, 'MT @nixon1788: Obama and the Left are disgusting anti Semitic pukes! #WithdrawUNFunding'

Is it possible to use the apriori algorithm to generate association rules? Does anyone know how to structure this data in order to use it or if it is even possible with the data I have?

...

ANSWER

Answered 2017-Jan-14 at 20:16

Here's an example as a starter:

Source https://stackoverflow.com/questions/41650651

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

Vulnerabilities

No vulnerabilities reported

Install SEMIT

Install pytorch

Support

If you run into any problems with this code, please submit a bug report on the Github site of the project. For another inquries pleace contact with me: yaxing@cvc.uab.es.

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