multilabel-image-classification-tensorflow | discuss two ways to perform image recognition | Data Labeling library

by isobar-us Python Version: Current License: MIT

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

kandi X-RAY | multilabel-image-classification-tensorflow Summary

multilabel-image-classification-tensorflow is a Python library typically used in Artificial Intelligence, Data Labeling, Deep Learning, Tensorflow, Keras, Docker applications. multilabel-image-classification-tensorflow has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However multilabel-image-classification-tensorflow build file is not available. You can download it from GitHub.

In this project we'll discuss two ways to perform image recognition:.

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Support

multilabel-image-classification-tensorflow has a low active ecosystem.

It has 46 star(s) with 23 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 4 have been closed. On average issues are closed in 6 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of multilabel-image-classification-tensorflow is current.

Quality

multilabel-image-classification-tensorflow has no bugs reported.

Security

multilabel-image-classification-tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

multilabel-image-classification-tensorflow is licensed under the MIT License. This license is Permissive.

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

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multilabel-image-classification-tensorflow releases are not available. You will need to build from source code and install.

multilabel-image-classification-tensorflow has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Base function for inceptionv2 .
Update a single step .
Inception V3 .
Setup the model .
Train an optimizer .
3 - layer inception .
Completes wiki processing .
Batch Multiclass NonMaxSuppression .
Transformer transformer layer .
Calculates the loss of a box classifier .

Get all kandi verified functions for this library.

multilabel-image-classification-tensorflow Key Features

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multilabel-image-classification-tensorflow Examples and Code Snippets

No Code Snippets are available at this moment for multilabel-image-classification-tensorflow.

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

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ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

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ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

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ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

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

Vulnerabilities

No vulnerabilities reported

Install multilabel-image-classification-tensorflow

You can download it from GitHub.
You can use multilabel-image-classification-tensorflow like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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