cnn-models | ImageNet pre-trained models | Machine Learning library

by cvjena Python Version: v1.0 License: BSD-2-Clause

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

kandi X-RAY | cnn-models Summary

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

ImageNet pre-trained models with batch normalization for the Caffe framework

Support

Quality

Security

License

Reuse

Support

cnn-models has a low active ecosystem.

It has 356 star(s) with 167 fork(s). There are 36 watchers for this library.

It had no major release in the last 12 months.

There are 8 open issues and 18 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cnn-models is v1.0

Quality

cnn-models has 0 bugs and 4 code smells.

Security

cnn-models has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cnn-models code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cnn-models is licensed under the BSD-2-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cnn-models releases are available to install and integrate.

cnn-models 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.

cnn-models saves you 36 person hours of effort in developing the same functionality from scratch.

It has 96 lines of code, 4 functions and 1 files.

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

Top functions reviewed by kandi - BETA

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

Construct a residual network
Helper function for residuals
Resolve residual tensor
Batch convolution layer

Get all kandi verified functions for this library.

cnn-models Key Features

No Key Features are available at this moment for cnn-models.

cnn-models Examples and Code Snippets

No Code Snippets are available at this moment for cnn-models.

Community Discussions

Trending Discussions on cnn-models

CNN for non-image data

Shape error when predicting with a trained model in tensorflow.keras

QUESTION

CNN for non-image data

Asked 2021-May-03 at 05:06

I am trying to create a model from this https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/ example that takes as inputs 3 (to unbug, there will be 1000s) inputs which are arrays of dimension (17,40):

...

ANSWER

Answered 2021-May-02 at 22:06

The Softmax layer size should be equal to the number of classes. Your Softmax layer has only 1 output. For this classification problem, first of all, you should turn your targets to a one-hot encoded format, then edit the size of the Softmax layer to the number of classes.

Source https://stackoverflow.com/questions/67361271

QUESTION

Shape error when predicting with a trained model in tensorflow.keras

Asked 2020-Nov-23 at 09:09

I'm creating a 1D CNN using tensorflow.keras, following this tutorial, with some of the concepts from this tutorial. So far modeling and training seem to be working, but I can't seem to generate a prediction. Here's an example of what I'm dealing with:

Data ...

ANSWER

Answered 2020-Nov-23 at 09:09

please runing the code: model.predict([trainX[0]]), and the model outputs the predicted results

Source https://stackoverflow.com/questions/64963461

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

Vulnerabilities

No vulnerabilities reported

Install cnn-models

You can download it from GitHub.
You can use cnn-models 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

The models are released under BSD 2-clause license allowing both academic and commercial use. I would appreciate if you give credit to this work by citing our paper in academic works and referencing to this Github repository in commercial works. If you need any support, please open an issue or contact Marcel Simon.

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