flower-recognition | Using state-of-the-art pre | Machine Learning library

by Gogul09 Python Version: Current License: MIT

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kandi X-RAY | flower-recognition Summary

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

Xception, Inception-v3, OverFeat, ResNet50, VGG16, VGG19. Update (16/12/2017): Included two new deep neural net models namely InceptionResNetv2 and MobileNet.

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flower-recognition has a low active ecosystem.

It has 142 star(s) with 98 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 10 have been closed. On average issues are closed in 133 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of flower-recognition is current.

Quality

flower-recognition has 0 bugs and 0 code smells.

Security

flower-recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

flower-recognition code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

flower-recognition 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

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

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

flower-recognition saves you 115 person hours of effort in developing the same functionality from scratch.

It has 291 lines of code, 0 functions and 4 files.

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

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flower-recognition Key Features

No Key Features are available at this moment for flower-recognition.

flower-recognition Examples and Code Snippets

No Code Snippets are available at this moment for flower-recognition.

Community Discussions

Trending Discussions on flower-recognition

Incompatible inputs of layers (ndim=4, found ndim=3)

QUESTION

Incompatible inputs of layers (ndim=4, found ndim=3)

Asked 2020-Jun-16 at 00:54

I tried to recreate this Flower Recognition CNN in Keras. The model seems to work, at least in the notebook (while getting predictions on the validation set), but I need to use the model somewhere else. Photos are 150x150 and this is how I build the CNN:

...

ANSWER

Answered 2020-Jun-16 at 00:54

Make sure you have the first dimension as 1 (1 image to predict). Your model is expecting the None dimension to be number of samples, 2nd + 3rd to be the image resolution, and 4th to be your RGB channels for the image.

data = np.expand_dims(data, axis=0)

Will add an extra dimension to your first axis.

See: How can I add new dimensions to a Numpy array?

Source https://stackoverflow.com/questions/62399263

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

Vulnerabilities

No vulnerabilities reported

Install flower-recognition

You can download it from GitHub.
You can use flower-recognition 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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