multi-label-classification-and-weakly-supervised-detection | course assignment about multi-label classification | Data Labeling library

by tsinghuaee29 Python Version: Current License: No License

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

kandi X-RAY | multi-label-classification-and-weakly-supervised-detection Summary

multi-label-classification-and-weakly-supervised-detection is a Python library typically used in Artificial Intelligence, Data Labeling applications. multi-label-classification-and-weakly-supervised-detection has no bugs, it has no vulnerabilities and it has low support. However multi-label-classification-and-weakly-supervised-detection build file is not available. You can download it from GitHub.

a course assignment about multi-label classification and weakly supervised detection

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Support

multi-label-classification-and-weakly-supervised-detection has a low active ecosystem.

It has 8 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 400 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of multi-label-classification-and-weakly-supervised-detection is current.

Quality

multi-label-classification-and-weakly-supervised-detection has 0 bugs and 0 code smells.

Security

multi-label-classification-and-weakly-supervised-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

multi-label-classification-and-weakly-supervised-detection code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

multi-label-classification-and-weakly-supervised-detection does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

multi-label-classification-and-weakly-supervised-detection releases are not available. You will need to build from source code and install.

multi-label-classification-and-weakly-supervised-detection has no build file. You will be need to create the build yourself to build the component from source.

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

It has 2364 lines of code, 160 functions and 42 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Calculates the loss of a training epoch
Perform a forward computation
Record the loss of the training
Parse command line arguments
Performs training on the model
Adjust the learning rate based on the optimizer
Validate the model
Evaluate the training
Evaluate training
Return the minimum between a and b
Return the maximum of a
Compute nms using nms
Calculate the map
Compute the average precision
Get configuration for optimizer
Preprocess an image
Clip the gradient of a given clip_norm
Generate a GPCC model
Import all functions
Perform the forward computation
Parse command line arguments
Locate CUDA
Construct correlation matrix
Calculate softmax softmax
Prints the optimizer
Forward features to the feature map
Visualize an image

Get all kandi verified functions for this library.

multi-label-classification-and-weakly-supervised-detection Key Features

No Key Features are available at this moment for multi-label-classification-and-weakly-supervised-detection.

multi-label-classification-and-weakly-supervised-detection Examples and Code Snippets

No Code Snippets are available at this moment for multi-label-classification-and-weakly-supervised-detection.

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?

...

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:

...

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.

...

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 multi-label-classification-and-weakly-supervised-detection

You can download it from GitHub.
You can use multi-label-classification-and-weakly-supervised-detection 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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