mobilenetv2 | An mxnet implementation of MobileNetV2 | Machine Learning library

by MTCloudVision Python Version: Current License: No License

X-Ray Key Features Code Snippets(2)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | mobilenetv2 Summary

mobilenetv2 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. mobilenetv2 has no bugs, it has no vulnerabilities and it has low support. However mobilenetv2 build file is not available. You can download it from GitHub.

An mxnet implementation of MobileNetV2

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Support

mobilenetv2 has a low active ecosystem.

It has 7 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 980 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of mobilenetv2 is current.

Quality

mobilenetv2 has no bugs reported.

Security

mobilenetv2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

mobilenetv2 does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

mobilenetv2 releases are not available. You will need to build from source code and install.

mobilenetv2 has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed mobilenetv2 and discovered the below as its top functions. This is intended to give you an instant insight into mobilenetv2 implemented functionality, and help decide if they suit your requirements.

get symbol
Residual residual .

Get all kandi verified functions for this library.

mobilenetv2 Key Features

No Key Features are available at this moment for mobilenetv2.

mobilenetv2 Examples and Code Snippets

Create a MobileNetV2 model .

python

Lines of Code : 21

License : Permissive (MIT License)

Copy

def create_model(input_shape):
    # load MobileNetV2
    model = MobileNetV2(input_shape=input_shape)
    # remove the last fully connected layer
    model.layers.pop()
    # freeze all the weights of the model except the last 4 layers
    for layer

Create a MobileNetV2 model .

python

Lines of Code : 20

License : Permissive (MIT License)

Copy

def create_mobilenet_model(input_shape, output_shape):
    model = MobileNetV2(input_shape=input_shape)
    # remove the last layer
    model.layers.pop()
    # freeze all the weights of the model except for the last 4 layers
    for layer in model.l

Community Discussions

Trending Discussions on mobilenetv2

Pytorch CPU CUDA device load without gpu

Issue retrieving error when adding a classifier to a MobileNet model

Why flatten() is not working in co-lab whereas it worked in kaggle-notebook posted by other user?

Retrieving low confidence score when adding my own classifier for MobileNetV2

Accuracy is dropped after adding a dense layer to a pretrained Mobilenet model

Gradients are None when trying to implement GradCam

A Keras model trains well, but predicts the same value

Replace stride layers in MobileNet application in Keras

Error when using a pretrained MobileNet after loading datas (dimension error) with Python3.x Tensorflow2

TensorFlow Keras Use MobileNetV2 Model with Inputs less than 32x32

QUESTION

Pytorch CPU CUDA device load without gpu

Asked 2021-Jun-11 at 12:41

I found this nice code Pytorch mobilenet which I cant get running on a CPU. https://github.com/rdroste/unisal

I am new to Pytorch so I am not shure what to do.

In line 174 of the module train.py the device is set:

...

ANSWER

Answered 2021-Jun-11 at 08:55

In https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-on-gpu-load-on-cpu you'll see there's a map_location keyword argument to send weights to the proper device:

Source https://stackoverflow.com/questions/67934005

QUESTION

Issue retrieving error when adding a classifier to a MobileNet model

Asked 2021-Jun-09 at 15:27

I have the following code, I am retrieving error when I try to add my own classifier.

...

ANSWER

Answered 2021-Jun-09 at 15:27

You should never mix keras and tf.keras. You can refer working code as shown below

Source https://stackoverflow.com/questions/67753569

QUESTION

Why flatten() is not working in co-lab whereas it worked in kaggle-notebook posted by other user?

Asked 2021-Jun-07 at 20:58

I am working on a project for pneumonia detection. I have looked over kaggle for notebooks on the same. there was a user who stacked two pretrained model densenet169 and mobilenet. I copies whole kaggle notebook from the user where he didn't get any error, but when I ran it in google colab I get this error in this part:

part where error is:

...

ANSWER

Answered 2021-Jun-07 at 20:58

You have mixed up your imports a bit.

Here is a fixed version of your code

Source https://stackoverflow.com/questions/67877688

QUESTION

Retrieving low confidence score when adding my own classifier for MobileNetV2

Asked 2021-May-30 at 20:10

I have the following code adding the classifier, I retrieve a low confidence score.

...

ANSWER

Answered 2021-May-30 at 17:23

When solving binary classification problems, if you are using sigmoid as your activation function, your dense layer should have 1 neuron instead of 2 neurons. If you use softmax, then your dense layer should have 2 neurons. You should choose between two of them and change the amount of output neuron accordingly. You can learn more about this in here.

Source https://stackoverflow.com/questions/67763791

QUESTION

Accuracy is dropped after adding a dense layer to a pretrained Mobilenet model

Asked 2021-May-29 at 01:01

I have the following code, utilising mobilenetv2 for two class classification. After adding the dense layer with 2 units, the accuracy is dropped significantly to 45%. I couldn't figure out what could be the issue, I changed optimiser but still accuracy didnt improve. My training dataset is 2000 with two categories, cat and dog.

...

ANSWER

Answered 2021-May-29 at 01:01

You can use the include_top=False statement when you load the pretrained model. This code statement removes the last part of the pretrained model.

Source https://stackoverflow.com/questions/67745283

QUESTION

Gradients are None when trying to implement GradCam

Asked 2021-May-20 at 19:33

I'm trying to implement GradCam like this Keras GradCam Tutorial.

I made a reduced version of it below. No matter what I do, my gradients are None.

...

ANSWER

Answered 2021-May-19 at 18:57

The reason for getting the None value is because you're computing the gradient w.r.t the input layer. Here is one simplistic solution of the above cases:

Source https://stackoverflow.com/questions/67607393

QUESTION

A Keras model trains well, but predicts the same value

Asked 2021-May-14 at 14:59

Let's try to make MobileNet V. 2 locate a bright band on a noisy image. Yes, it is overkill to use a deep convolutional network for such a tack, but originally it was intended just like a smoke test to make sure the model works. We will train it on synthetic data:

...

ANSWER

Answered 2021-May-14 at 14:59

The problem was that a network reasonably functioning in the training mode failed to work in the inference mode. What might be the cause? There are basically two layer types working differently in the two modes: dropout and batch normalization. In MobileNet V. 2, we have only batch normalization, so let's consider how it works.

In the training mode a BN layer calculates batch mean and variance and normalizes the data using these batch values. At the same time it remembers the mean and variance as a moving average weighted with a coefficient called momentum.

Source https://stackoverflow.com/questions/67502042

QUESTION

Replace stride layers in MobileNet application in Keras

Asked 2021-May-09 at 06:39

I would like to apply in Keras MobileNetV2 on images of size 39 x 39 to classify 3 classes. My images represent heat maps (e.g. what keys have been pressed on the keyboard). I think MobileNet was designed to work on images of size 224 x 224. I will not use transfer learning but train the model from scratch.

To make MobileNet work on my images, I would like to replace the first three stride 2 convolutions with stride 1. I have the following code:

...

ANSWER

Answered 2021-May-09 at 06:24

Here is one workaround for your need but I think probably it's possible to have a more general approach. However, in the MobileNetV2, there is only one conv layer with strides 2. If you follow the source code, here

Source https://stackoverflow.com/questions/67453434

QUESTION

Error when using a pretrained MobileNet after loading datas (dimension error) with Python3.x Tensorflow2

Asked 2021-Apr-07 at 01:28

My dataset is a folder of training data and a folder of validation data. In each folder (train and valid) there are .npy files. Each file is (s,256,256,3) (s frames in the volume, 3 channels, and 256 the same height and width). And there is a CSV file with a label for each file.

Here is my code to load data :

...

ANSWER

Answered 2021-Apr-06 at 12:41

Try changing the batch_input_shape in line:

Source https://stackoverflow.com/questions/66966981

QUESTION

TensorFlow Keras Use MobileNetV2 Model with Inputs less than 32x32

Asked 2021-Apr-02 at 16:34

I want to use the MobileNetV2 model without weights with a size less than 32x32. If I try

...

ANSWER

Answered 2021-Apr-02 at 16:34

Create a new model from MobileNet with the first 30 layers as outputs (i.e. a multiple output model). Here is how:

Source https://stackoverflow.com/questions/66863736

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

Vulnerabilities

No vulnerabilities reported

Install mobilenetv2

You can download it from GitHub.
You can use mobilenetv2 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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