MobileNet-V2 | A Complete and Simple Implementation of MobileNet-V2 | Machine Learning library

by MG2033 Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | MobileNet-V2 Summary

MobileNet-V2 is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. MobileNet-V2 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

A Complete and Simple Implementation of MobileNet-V2 in PyTorch

Support

Quality

Security

License

Reuse

Support

MobileNet-V2 has a low active ecosystem.

It has 207 star(s) with 44 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 4 have been closed. On average issues are closed in 24 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of MobileNet-V2 is current.

Quality

MobileNet-V2 has 0 bugs and 0 code smells.

Security

MobileNet-V2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

MobileNet-V2 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

MobileNet-V2 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

MobileNet-V2 releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

MobileNet-V2 saves you 174 person hours of effort in developing the same functionality from scratch.

It has 430 lines of code, 26 functions and 7 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed MobileNet-V2 and discovered the below as its top functions. This is intended to give you an instant insight into MobileNet-V2 implemented functionality, and help decide if they suit your requirements.

Train the model .
Initialize network settings .
Parse the arguments .
Main function .
Create the experiment directories .
Plots random images .
Creates an InvertedResidualResidualBlock sequence .
2D conv2d convolution2d .
Reset the statistics .
Calculates the mean and standard deviation of a dataset .

Get all kandi verified functions for this library.

MobileNet-V2 Key Features

No Key Features are available at this moment for MobileNet-V2.

MobileNet-V2 Examples and Code Snippets

No Code Snippets are available at this moment for MobileNet-V2.

Community Discussions

Trending Discussions on MobileNet-V2

Issue retrieving ValueError: `decode_predictions` expects a batch of predictions

Cannot save a model when a regularizer is added to a model from tf.keras.applications

How to convert Tensorflow Object Detection API model to TFLite?

QUESTION

Issue retrieving ValueError: `decode_predictions` expects a batch of predictions

Asked 2021-Mar-21 at 22:15

I have the following code took it from Github to run a pre-trained model mobilenet_v2 https://github.com/vvigilante/mobilenet_v2_keras/blob/master/mobilenet_v2_keras.py and trying to run it, however, I am facing some issue to run the code. I tried to import it from Keras. applications.mobilentnetv2 but it didn't resolve the issue.

...

ANSWER

Answered 2021-Mar-21 at 21:13

This function is meant to transform a vector of 1,000 probabilities into a category of the ImageNet dataset, which has 1,000 categories. Your final layer has 100 categories, so the function is confused. You could do this:

Source https://stackoverflow.com/questions/66736103

QUESTION

Cannot save a model when a regularizer is added to a model from tf.keras.applications

Asked 2020-Oct-30 at 13:18

I use python 3.7 with Tensorflow 2.2.0. I would like to train a MobileNet-V2 from scratch, therefore I take the already built model and add a regularizer to all required layers. The model compiles well and I am able to fit it. However, when saving the model, I encounter the following error:

...

ANSWER

Answered 2020-Oct-30 at 13:18

It turns out, the lambda function should not take any parameters, according to the documentation. However, when I was not passing the parameters, it resulted in another very strange error (it was about the Dense layer not having the depthwise_kernel, which is obvious and should not have happened, but somehow it happened). Digging deeper, I found this is a bug which is well explained in here.

So, my final solution was to define two separate functions for regularization as is suggested in the link above.

Source https://stackoverflow.com/questions/64538733

QUESTION

How to convert Tensorflow Object Detection API model to TFLite?

Asked 2020-Sep-29 at 14:11

I am trying to convert a Tensorflow Object Detection model(ssd-mobilenet-v2-fpnlite, from TensorFlow 2 Detection Model Zoo) to TFLite. First of all, I train the model using the model_main_tf2.py and then I use the export_tflite_graph_tf2.py to export a saved model(.pb). However, when it comes to convert the .pb file to .tflite it throws this error:

...

ANSWER

Answered 2020-Sep-29 at 12:43

I don't have enough reputation to just comment but the problem here seems to be your SAVED_MODEL_PATH.

You could try to hardcode the path and remove the .pb file. I don't remember exactly what's the trick here but it's definitively due to the path

Source https://stackoverflow.com/questions/64118928

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

Vulnerabilities

No vulnerabilities reported

Install MobileNet-V2

You can download it from GitHub.
You can use MobileNet-V2 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 .

Find more information at: