keras-inceptionV4 | Keras Implementation of Google 's Inception-V4 Architecture | Machine Learning library

by kentsommer Python Version: 2.1 License: Apache-2.0

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kandi X-RAY | keras-inceptionV4 Summary

keras-inceptionV4 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-inceptionV4 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However keras-inceptionV4 build file is not available. You can download it from GitHub.

Keras Implementation of Google's Inception-V4 Architecture (includes Keras compatible pre-trained weights)

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keras-inceptionV4 has a low active ecosystem.

It has 461 star(s) with 182 fork(s). There are 21 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 27 have been closed. On average issues are closed in 305 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-inceptionV4 is 2.1

Quality

keras-inceptionV4 has 0 bugs and 13 code smells.

Security

keras-inceptionV4 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-inceptionV4 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-inceptionV4 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

keras-inceptionV4 releases are available to install and integrate.

keras-inceptionV4 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.

keras-inceptionV4 saves you 296 person hours of effort in developing the same functionality from scratch.

It has 713 lines of code, 34 functions and 11 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed keras-inceptionV4 and discovered the below as its top functions. This is intended to give you an instant insight into keras-inceptionV4 implemented functionality, and help decide if they suit your requirements.

Get weights from inception
Return a list of layers
Processes an image
Crop the image
Preprocess input
Set weights
Return the natural keys of the object
Convert a string to an integer
Create an inception model
Base function
Inception V4
Perform the blockinception of the image
Perform blockinceptioning
Perform block invasion
Block - reduction op
2D convolutional layer
Block reduction op

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keras-inceptionV4 Key Features

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keras-inceptionV4 Examples and Code Snippets

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Community Discussions

Trending Discussions on keras-inceptionV4

InceptionResnetV2 STEM block keras implementation mismatch the one in the original paper?

QUESTION

InceptionResnetV2 STEM block keras implementation mismatch the one in the original paper?

Asked 2020-Oct-27 at 08:00

I've been trying to compare the InceptionResnetV2 model summary from Keras implementation with the one specified in their paper, and it doesn't seem to show much resemblance when it comes to the filter_concat block.

The first lines of the model summary() are as shown below. (for my case, the input is changed to 512x512, but up to my knowledge, it doesn't affect the number of filters per layer, so we can also use them to follow up the paper-code translation):

...

ANSWER

Answered 2020-Oct-27 at 08:00

It achieves similar results.

I just received an e-mail confirming the error from Alex Alemi, Senior Research Scientist at Google and original publisher of the blog post regarding the release of the code for InceptionResnetV2. It seems that during internal experiments, the STEM blocks were switched and the release just kept like that.

Cite:

Dani Azemar,

It seems you're right. Not entirely sure what happened but the code is obviously the source of truth in the sense that the released checkpoint is for the code that is also released. When we were developing the architecture we did a whole slew of internal experiments and I imagine at some point the stems were switched. Not sure I have the time to dig deeper at the moment, but like I said, the released checkpoint is a checkpoint for the released code as you can verify yourself by running the evaluation pipeline. I agree with you that it seems like this is using the original Inception V1 stem. Best Regards,

Alex Alemi

I'll update this post with changes regarding this subject.

UPDATE: Christian Szegedy, also publisher of the original paper, just tweeted me:

The original experiments and model was created in DistBelief, a completely different framework pre-dating Tensorflow.

The TF version was added a year later and might have had discrepancies from the original model, however it was made sure to achieve similar results.

So, since it achieves similar results, your experiments would be roughly the same.

Source https://stackoverflow.com/questions/64488034

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

Vulnerabilities

No vulnerabilities reported

Install keras-inceptionV4

You can download it from GitHub.
You can use keras-inceptionV4 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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