Inception-v4 | Inception-v4 , Inception - Resnet-v1 and v2 Architectures | Machine Learning library

by titu1994 Python Version: v1.2 License: MIT

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kandi X-RAY | Inception-v4 Summary

Inception-v4 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. Inception-v4 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Inception-v4 build file is not available. You can download it from GitHub.

Implementations of the Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras using the Functional API. The paper on these architectures is available at "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning". The models are plotted and shown in the architecture sub folder. Due to lack of suitable training data (ILSVR 2015 dataset) and limited GPU processing power, the weights are not provided.

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Inception-v4 has a low active ecosystem.

It has 355 star(s) with 167 fork(s). There are 19 watchers for this library.

It had no major release in the last 12 months.

There are 5 open issues and 6 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Inception-v4 is v1.2

Quality

Inception-v4 has 0 bugs and 0 code smells.

Security

Inception-v4 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Inception-v4 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Inception-v4 is licensed under the MIT License. This license is Permissive.

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

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Inception-v4 releases are available to install and integrate.

Inception-v4 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.

Inception-v4 saves you 187 person hours of effort in developing the same functionality from scratch.

It has 462 lines of code, 22 functions and 3 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed Inception-v4 and discovered the below as its top functions. This is intended to give you an instant insight into Inception-v4 implemented functionality, and help decide if they suit your requirements.

Create an inception model
Generate the inception stem
Resolve inception C
Resolve inception image
Convolutional layer
R Compute the reduction op
Reduce the input image
Convolution block layer
Create inception resnet v1
The inception resnet stem
Create inception resnet
Resnet activation resnet
Creates an inception resnet
Resnet v2
The inception resnet v2
Resnet V2

Get all kandi verified functions for this library.

Inception-v4 Key Features

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Inception-v4 Examples and Code Snippets

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Community Discussions

Trending Discussions on Inception-v4

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 Inception-v4

You can download it from GitHub.
You can use Inception-v4 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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