caffe-train | Machine Learning library

by ruimashita Python Version: Current License: No License

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

kandi X-RAY | caffe-train Summary

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

caffe-train

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caffe-train has a low active ecosystem.

It has 45 star(s) with 87 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1099 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of caffe-train is current.

Quality

caffe-train has 0 bugs and 0 code smells.

Security

caffe-train has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

caffe-train code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

caffe-train 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

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

caffe-train 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.

It has 251 lines of code, 24 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Split train val .
Create HDF5 file .
Augment image .
Create an LDM database .
Resets training and validation directory .
Resets training directories .
check if given image is a verify image
Make the lmdb files .
Writes the labels to the csv file .
Return a list of all images .

Get all kandi verified functions for this library.

caffe-train Key Features

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caffe-train Examples and Code Snippets

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Community Discussions

Trending Discussions on caffe-train

(-215:Assertion failed) 1 <= blobs.size() && blobs.size() <= 2

QUESTION

(-215:Assertion failed) 1 <= blobs.size() && blobs.size() <= 2

Asked 2018-Sep-18 at 13:27

I'm trying to import my own pre-trained Caffe Googlenet model using OpenCV v.3.4.3, where i run a Caffe test after training using the model deploy file and everything was working fine. However when feeding the OpenCv net (after loading it) with image blob i get an exception.

OpenCv Code:

...

ANSWER

Answered 2018-Sep-18 at 13:27

This issue was solved for me here: https://github.com/opencv/opencv/issues/12578#issuecomment-422304736

"there is no loss3/classifier_retrain from Caffe-deploy-model.prototxt in Caffe-train-val-model.prototxt. If you tried to run this model several times in Caffe you'll get different outputs for the same input because Caffe fills missed weights randomly."

Copyright (github.com/dkurt)

Source https://stackoverflow.com/questions/52380819

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

Vulnerabilities

No vulnerabilities reported

Install caffe-train

You can download it from GitHub.
You can use caffe-train 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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