caffe-model | Caffe models ( imagenet pretrain | Machine Learning library

by GeekLiB Python Version: Current License: No License

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kandi X-RAY | caffe-model Summary

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

This folder contains the deploy files(include generator scripts) and pre-train models of resnet-v1, resnet-v2, inception-v3, inception-resnet-v2 and densenet(coming soon). We didn't train any model from scratch, some of them are converted from other deep learning framworks (inception-v3 from mxnet, inception-resnet-v2 from tensorflow), some of them are converted from other modified caffe (resnet-v2). But to achieve the original performance, finetuning is performed on imagenet for several epochs. The main contribution belongs to the authors and model trainers.

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caffe-model has a low active ecosystem.

It has 124 star(s) with 93 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of caffe-model is current.

Quality

caffe-model has 0 bugs and 67 code smells.

Security

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

caffe-model code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

caffe-model 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.

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

caffe-model has no build file. You will be need to create the build yourself to build the component from source.

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

caffe-model saves you 1409 person hours of effort in developing the same functionality from scratch.

It has 3151 lines of code, 134 functions and 18 files.

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

Top functions reviewed by kandi - BETA

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

Create inception_bn
Factorization factorization
Constructs an inner product of a bottom - up layer
Factorization
Create inception v3
Conv3 layer
A 2x3 B
3x3 convolutional problem
Convert inception resnet v2
Resnet convolution residuals
Resnet convolution residual
Create inception v1
Convolution layer
Constructs a NetSpec proto
Conv4
First activation resnet
Create alexnet proto
First convolutional layer
Conv4 convolutional CNN
Alexnet layer
Convolutional network
Builds a VGG - 16 - layer
Create a VGG - 16 core layer
Version of VGG - 19
Convert inception v4
Convolution residuals

Get all kandi verified functions for this library.

caffe-model Key Features

No Key Features are available at this moment for caffe-model.

caffe-model Examples and Code Snippets

No Code Snippets are available at this moment for caffe-model.

Community Discussions

Trending Discussions on caffe-model

How to present ndarray to trained caffe net in python?

QUESTION

How to present ndarray to trained caffe net in python?

Asked 2019-Apr-26 at 12:34

I'm trying to use a fully-connceted caffe neural network (NN) in python. The original model/NN was implemented and trained in Keras and then converted to a caffe model using MMdnn.

The data I want to present to the NN is a numpy array. It should push that trough the network and then make a class prediction on the output.

However, when I'm trying to present 1-D numpy array to the loaded caffe-model, I'm getting the following error:

...

ANSWER

Answered 2019-Apr-26 at 12:34

Found the answer, why I couldn't input the numpy array the way I tried. It's neccessary to assign it to the right input blob of the net (as I understood).

See question for layer names.

This code works:

Source https://stackoverflow.com/questions/55843935

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

Vulnerabilities

No vulnerabilities reported

Install caffe-model

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