noisy_label | CVPR15 paper Learning from Massive Noisy Labeled Data | Machine Learning library

by Cysu Python Version: Current License: No License

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

kandi X-RAY | noisy_label Summary

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

Code for the CVPR15 paper "Learning from Massive Noisy Labeled Data for Image Classification"

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Support

noisy_label has a low active ecosystem.

It has 80 star(s) with 28 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 796 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of noisy_label is current.

Quality

noisy_label has 0 bugs and 0 code smells.

Security

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

noisy_label code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

noisy_label 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

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

noisy_label 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.

noisy_label saves you 179 person hours of effort in developing the same functionality from scratch.

It has 442 lines of code, 32 functions and 8 files.

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

Top functions reviewed by kandi - BETA

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

Make a numpy array from data_root
Compute the confusion matrix
Read a file
Write array to file
Save data to a protobuf
Read values from a file
Compute confusion matrix
Generate a random matrix q
Write key value pairs to file
Write a matrix to a file
Parse the contents of a file
Create training and test data
Calculates the noise types
Create a clean function
Corrupts the given labels
Creates a mixed mixture
Unpickles a file

Get all kandi verified functions for this library.

noisy_label Key Features

No Key Features are available at this moment for noisy_label.

noisy_label Examples and Code Snippets

No Code Snippets are available at this moment for noisy_label.

Community Discussions

Trending Discussions on noisy_label

Tensorflow: how to create batch with different type of data from different source (folder)?

QUESTION

Tensorflow: how to create batch with different type of data from different source (folder)?

Asked 2017-Aug-24 at 18:33

I can't find an appropriate question title, sorry.

I have a graph composed by two main data flow: image classification and label cleaning. I have two type of data:

(image_data, noisy_label, verified_label) from validation set
(image_data, noisy_label) from train set

The first is used to train the label cleaning part of the graph. The second is used to train the image classification after its noisy label is cleaned.

Every batch need to have a ratio of 1:9.

How can i create this type of batch?? is it possible in tensorflow??

...

ANSWER

Answered 2017-Aug-24 at 18:33

I solved the ratio problem!! I create two batch, one for validation, one for train. Then i concatenate them with image_batch = tf.concat([image_validation_batch, image_train_batch], 0). This is only for image batch, i will investigate on the label.

Source https://stackoverflow.com/questions/45266976

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

Vulnerabilities

No vulnerabilities reported

Install noisy_label

Setup an experiment directory. You can either create a new one under external/, or make a link to another existing directory.
Clone this repository # Make sure to clone with --recursive to get the modified Caffe git clone --recursive https://github.com/Cysu/noisy_label.git
Build the Caffe cd external/caffe # Now follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html # If you're experienced with Caffe and have all of the requirements installed # and your Makefile.config in place, then simply do: make -j8 && make py cd -
Setup an experiment directory. You can either create a new one under external/, or make a link to another existing directory. mkdir -p external/exp or ln -s /path/to/your/exp/directory external/exp

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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