edge-connect | EdgeConnect : Structure Guided Image Inpainting using Edge | Machine Learning library

by knazeri Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | edge-connect Summary

edge-connect is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Generative adversarial networks applications. edge-connect has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However edge-connect has a Non-SPDX License. You can download it from GitHub.

EdgeConnect: Structure Guided Image Inpainting using Edge Prediction, ICCV 2019

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Quality

Security

License

Reuse

Support

edge-connect has a medium active ecosystem.

It has 2384 star(s) with 511 fork(s). There are 68 watchers for this library.

It had no major release in the last 6 months.

There are 101 open issues and 71 have been closed. On average issues are closed in 13 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of edge-connect is current.

Quality

edge-connect has 0 bugs and 12 code smells.

Security

edge-connect has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

edge-connect code analysis shows 0 unresolved vulnerabilities.

There are 5 security hotspots that need review.

License

edge-connect has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

edge-connect 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, examples and code snippets are available.

edge-connect saves you 638 person hours of effort in developing the same functionality from scratch.

It has 1482 lines of code, 79 functions and 16 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Evaluate the model
Sample from validation set
Concatenate two input images
Saves the model
Performs postprocessing
Update the metrics
Calculates FID for given paths
Calculate activations
Calculate the distance between two Gaussians
Calculate the mean and variance of activations
Compute the statistics for the given path
Load configuration
Parse command line arguments
Print model configuration
Parse command line arguments
Runs the test
Loads the model
Compute the mean distance between two images

Get all kandi verified functions for this library.

edge-connect Key Features

No Key Features are available at this moment for edge-connect.

edge-connect Examples and Code Snippets

EdgeConnect-CoreML,Usage

Swift

Lines of Code : 1

License : Non-SPDX (NOASSERTION)

Copy

python test.py --checkpoints ./checkpoints/places2 --input ./examples/test/places2 --output ./results

Community Discussions

Trending Discussions on edge-connect

Distributing a Neo4j Graph Across Multiple Servers

edge-connectivity with constraints on number of vertices and removed edges

QUESTION

Distributing a Neo4j Graph Across Multiple Servers

Asked 2020-Nov-06 at 13:02

Is it possible to distribute a Neo4j graph across multiple servers where vertex A on server 1 is edge-connected to vertex B on server 2?

All I can find so far are discussions on Neo4j's sharding but I can't find anywhere where it is specifically stated that edges can span servers.

...

ANSWER

Answered 2020-Nov-04 at 23:56

No, there is no way for a relationship to connect nodes on different servers.

If such a capability existed, it would be significantly slower than having all the data colocated, and also suffer a higher rate of query failure (due to things like timeouts and dropped connections).

Source https://stackoverflow.com/questions/64688155

QUESTION

edge-connectivity with constraints on number of vertices and removed edges

Asked 2019-Dec-10 at 17:56

I need to find a subset G' of a graph G that can be disconnected from G by removing some edges. I have some constraints on number of vertices and edges

Number of removed edges should be less than e
Number of vertices in G' should be greater than v

I believe this problem should be a version of minimum-cut max-flow and/or edge-connectivity in graph theory. I wonder if there are already some studies (exact or heuristic alg) investigating this problem?

Any help or suggestions would be really appreciated.

...

ANSWER

Answered 2019-Dec-10 at 17:56

A version of this problem has been studied in the literature as "vertex separator" and "edge separator" problems. There is still room for improvement. The followings are some useful links:

I hope this helps.

Source https://stackoverflow.com/questions/44140287

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

Vulnerabilities

No vulnerabilities reported

Install edge-connect

Clone this repo:
Install PyTorch and dependencies from http://pytorch.org
Install python requirements:
Download the pre-trained models using the following links and copy them under ./checkpoints directory. Places2 | CelebA | Paris-StreetView.

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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