SCDA | Adapting Object Detectors via Selective Cross-Domain | Computer Vision library

by xinge008 Python Version: Current License: No License

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

kandi X-RAY | SCDA Summary

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

The project of our work "Adapting Object Detectors via Selective Cross-Domain Alignment" (CVPR2019).

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Quality

Security

License

Reuse

Support

SCDA has a low active ecosystem.

It has 62 star(s) with 10 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 4 have been closed. On average issues are closed in 0 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SCDA is current.

Quality

SCDA has 0 bugs and 0 code smells.

Security

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

SCDA code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SCDA 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

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

SCDA 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.

Top functions reviewed by kandi - BETA

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

Train the model
Print information about the dataset
Generate a hard label
Average gradients
Performs the prediction
Calculate rcNN loss
Smooth l1 loss with sigma
Compute accuracy
Visualize a single image
Assign arguments to each level
Download images to tarDir
Predict masks for the given image
Generate results for vispy
Compute bounding box
Return a list of category ids
Assigns proposals to layer index
Build builder
Calculate the map map for a given set of data
Compute the similarity between two images
Print information about the dataset
Show a set of annions
Validate a model
Compute mask targets based on ground truth
Concatenate a single image
Validate a single image
Build data loader
Evaluate COC from results

Get all kandi verified functions for this library.

SCDA Key Features

No Key Features are available at this moment for SCDA.

SCDA Examples and Code Snippets

No Code Snippets are available at this moment for SCDA.

Community Discussions

Trending Discussions on SCDA

How can I translate the "Hello ML.NET World" example into F#?

QUESTION

How can I translate the "Hello ML.NET World" example into F#?

Asked 2020-May-08 at 03:09

I'm attempting to translate the "Hello ML.NET World" example from C# to F# (code copied below), but I'm getting an F# compiler error about incompatible types.

I've seen a couple blog posts about ML.NET and F#, but they all use the older API which involved explicitly creating a LearningPipeline object. As far as I can tell, this API has been removed.

The problematic line in C# is the line to train a pipeline:

...

ANSWER

Answered 2020-May-07 at 18:12

I've also struggled with this. Try with this helper function:

Source https://stackoverflow.com/questions/61663860

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

Vulnerabilities

No vulnerabilities reported

Install SCDA

PyTorch 0.4.1 (also test with 0.4.0)
torchvision 0.2.1
OpenCV
scikit-learn
Cython
GCC >= 4.9 (test with 5.4)
cd extensions; ./build_all.sh
cd datasets/pycocotools & make (install pycocotools according to the guideline)

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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