retrieval-2016-deepvision | Faster R-CNN features for Instance Search

by imatge-upc Python Version: Current License: MIT

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

kandi X-RAY | retrieval-2016-deepvision Summary

retrieval-2016-deepvision is a Python library. retrieval-2016-deepvision has no vulnerabilities, it has a Permissive License and it has low support. However retrieval-2016-deepvision has 1 bugs and it build file is not available. You can download it from GitHub.

Faster R-CNN features for Instance Search

Support

Quality

Security

License

Reuse

Support

retrieval-2016-deepvision has a low active ecosystem.

It has 221 star(s) with 94 fork(s). There are 21 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 8 have been closed. On average issues are closed in 44 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of retrieval-2016-deepvision is current.

Quality

retrieval-2016-deepvision has 1 bugs (0 blocker, 0 critical, 0 major, 1 minor) and 75 code smells.

Security

retrieval-2016-deepvision has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

retrieval-2016-deepvision code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

retrieval-2016-deepvision 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

retrieval-2016-deepvision releases are not available. You will need to build from source code and install.

retrieval-2016-deepvision 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.

retrieval-2016-deepvision saves you 345 person hours of effort in developing the same functionality from scratch.

It has 826 lines of code, 44 functions and 8 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed retrieval-2016-deepvision and discovered the below as its top functions. This is intended to give you an instant insight into retrieval-2016-deepvision implemented functionality, and help decide if they suit your requirements.

Visualize a single query
Return a dictionary of parameters .
Rank the top n images .
Get the feature for a query .
Returns the query vectors for each query .
Extracts the features from the database .
Calculate the average accuracy .
Load query info from file .
Compute the relnotations for a given query .
Creates a thumbnail

Get all kandi verified functions for this library.

retrieval-2016-deepvision Key Features

No Key Features are available at this moment for retrieval-2016-deepvision.

retrieval-2016-deepvision Examples and Code Snippets

No Code Snippets are available at this moment for retrieval-2016-deepvision.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install retrieval-2016-deepvision

You need to download and install Faster R-CNN python implementation by Ross Girshick. Point params['fast_rcnn_path'] to the Faster R-CNN root path in params.py.
Download Oxford and Paris Buildings datasets. There are scripts under data/images/paris and data/images/oxford/ that will do that for you.
Download Faster R-CNN models by running data/models/fetch_models.sh.

Support

If you have any general doubt about our work or code which may be of interest for other researchers, please use the public issues section on this github repo. Alternatively, drop us an e-mail at amaia.salvador@upc.edu or xavier.giro@upc.edu.

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