RGB-N | synthetic dataset generation for the CVPR 2018 paper | Machine Learning library

by pengzhou1108 Python Version: Current License: MIT

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

kandi X-RAY | RGB-N Summary

RGB-N is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. RGB-N has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Code and synthetic dataset generation for the CVPR 2018 paper "Learning Rich Features for Image Manipulation Detection".

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Support

RGB-N has a low active ecosystem.

It has 136 star(s) with 47 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 21 open issues and 11 have been closed. On average issues are closed in 71 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of RGB-N is current.

Quality

RGB-N has no bugs reported.

Security

RGB-N has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

RGB-N 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

RGB-N releases are not available. You will need to build from source code and install.

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

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

Top functions reviewed by kandi - BETA

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

Builds the resnet network
Compact bilinear pooling layer
Compute the FFT
Generate a sparse matrix
Finds anchors in image layer
Compute the targets from the input image
Transform a bounding box
Build the resnet network
Build the base network
Train a network
Generate anchors for training image
Create a cfg from a list
Compute the results of a MATLAB eval
Compute results using the MATLAB eval code
Return the path to the output directory
Returns the output directory
Compute the results using the MATLAB eval code
Builds the VGG network
Create the architecture
Convert from recommre to TRA
Displays the detection of the given image
Get a combined ROI model from the given images
Parse command line arguments
Locate CUDA
Create proposal target layer
Compute proposal layer
Compute proposal top layer

Get all kandi verified functions for this library.

RGB-N Key Features

No Key Features are available at this moment for RGB-N.

RGB-N Examples and Code Snippets

No Code Snippets are available at this moment for RGB-N.

Community Discussions

Trending Discussions on RGB-N

Adding custom TensorFlow OP

Specifying a specific hex color for individual points in a 3d scatter plot

How make a 3x4 color correction matrix with least-square manner?

QUESTION

Adding custom TensorFlow OP

Asked 2021-Jan-07 at 18:20

I am trying to use the Tensorflow implementation of compact bilinear pooling by ronghanghu since it's used in the implementation of the "Learning Rich Features for Image Manipulation Detection" paper. ronghanghu uses TensorFlow version 1.12.0 with CUDA 8.0 and g++ 5.4.0 to build sequential_batch_fft.so. However, they do say we can rebuild the sequential_batch_fft.so using a different version of Tensorflow (in my case 2.4.0) with a different compiler (g++ 7.5.0) and a different CUDA version (11.0). When I try to build sequential_batch_fft.so using the commands in compile.sh below

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ANSWER

Answered 2021-Jan-07 at 18:20

Turns out the problem was with the tensorflow_framework.so. Was able to fix this by creating a symbolic link ln -s libtensorflow_framework.so.2 libtensorflow_framework.so in ../site-packages/tensorflow. Also, I think my project path was not suitable as it contains spaces. Not so sure about this one but when I used TensorFlow installed on a path with no spaces everything worked fine.

Source https://stackoverflow.com/questions/65599567

QUESTION

Specifying a specific hex color for individual points in a 3d scatter plot

Asked 2018-Apr-25 at 03:20

I have LiDAR dataset on which I've extracted RGB-NIR colors from a raster. I'm having no issues generating the 3d plots in plotly I want to make, or coloring the plot based on a 1-d column of values (such as NDVI); but I'm interested in coloring the points based on an RGB hex code I've written into one of the columns (taken from the raster), but i can't seem to find a way to write this in as a colorspace. While I can call on individual hex codes, it treats them as factors rather than a color specification. What I've figured out that I can do is either a) specify a qualitative color, or b) apply a color ramp. I can't seem to find a way to apply an RGB colorspace to the data.

Is there a way to specify a column in a data.frame (working in R) which has a hex or RGB code associated with each individual point in a plotly scatter plot? Is my only alternative to make a 3 color (R-G-B) color ramp, and map colors to it? Is there any way to get RGB colors to a plotly marker?

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ANSWER

Answered 2018-Apr-25 at 03:20

According to ?plot_ly():

color (...) To avoid scaling, wrap with I()

Modifying your code to color =~ I(HEX) seems to work. It's hard for me to tell, though, since all of the colors appear to be very similar to one another (Five Shades of Grey...)

Source https://stackoverflow.com/questions/50012363

QUESTION

How make a 3x4 color correction matrix with least-square manner?

Asked 2017-Jun-23 at 17:01

I know this question will sound simple but yes, I want to get a 3x4 Color Correction Matrix (CCM) with least-square method for this data b=M*A where its dimensions are b_3x10, M_3x4 and A_4x10 Here the values:

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ANSWER

Answered 2017-Jun-23 at 14:43

Usually in least-squares problems, we solve for A*x = b, but in your case the situation is such that the coefficients you want to solve for appear at the beginning of the expression: x*A = b. Note that we can reformulate this into the standard form by transposing: x.' * A.' = b.'. After that you can use MATLAB's mldivide operator, but you'll have to put it in the above form, then transpose the result when you're finished:

Source https://stackoverflow.com/questions/44723468

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

Vulnerabilities

No vulnerabilities reported

Install RGB-N

You can download it from GitHub.
You can use RGB-N 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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