vit-keras | Keras implementation of ViT | Computer Vision library

by faustomorales Python Version: 0.1.2 License: Apache-2.0

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

kandi X-RAY | vit-keras Summary

vit-keras is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, Keras applications. vit-keras has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However vit-keras build file is not available. You can install using 'pip install vit-keras' or download it from GitHub, PyPI.

Keras implementation of ViT (Vision Transformer)

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Support

vit-keras has a low active ecosystem.

It has 281 star(s) with 71 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 4 open issues and 29 have been closed. On average issues are closed in 25 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of vit-keras is 0.1.2

Quality

vit-keras has 0 bugs and 5 code smells.

Security

vit-keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

vit-keras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

vit-keras is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

vit-keras releases are available to install and integrate.

Deployable package is available in PyPI.

vit-keras 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.

It has 444 lines of code, 23 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Load weights from a numpy array .
Build Keras model .
Compute the attention map .
Builds a VIT model .
Builds a VIT model .
Create a LIT - L16 model .
Builds a LTI model .
Reads data from file .
Applies embedding weights to target layer .
Load pretrained pretrained pretrained weights .

Get all kandi verified functions for this library.

vit-keras Key Features

No Key Features are available at this moment for vit-keras.

vit-keras Examples and Code Snippets

No Code Snippets are available at this moment for vit-keras.

Community Discussions

Trending Discussions on vit-keras

How to extract features using VGG16 model and use them as input for another model(say resnet, vit-keras, etc)?

QUESTION

How to extract features using VGG16 model and use them as input for another model(say resnet, vit-keras, etc)?

Asked 2022-Mar-02 at 15:57

I am a bit new at Deep learning and image classification. I want to extract features from an image using VGG16 and give them as input to my vit-keras model. Following is my code:

...

ANSWER

Answered 2022-Mar-02 at 15:57

You cannot feed the output of the VGG16 model to the vit_model, since both models expect the input shape (224, 224, 3) or some shape that you defined. The problem is that the VGG16 model has the output shape (8, 8, 512). You could try upsampling / reshaping / resizing the output to fit the expected shape, but I would not recommend it. Instead, just feed the same input to both models and concatenate their results afterwards. Here is a working example:

Source https://stackoverflow.com/questions/71324609

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

Vulnerabilities

No vulnerabilities reported

Install vit-keras

You can install using 'pip install vit-keras' or download it from GitHub, PyPI.
You can use vit-keras 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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