EfficientNet | Gluon implementation of EfficientNet and EfficientNet-lite | Machine Learning library

by mnikitin Python Version: Current License: No License

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kandi X-RAY | EfficientNet Summary

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

Gluon implementation of EfficientNet and EfficientNet-lite

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EfficientNet has a low active ecosystem.

It has 30 star(s) with 11 fork(s). There are 5 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 151 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of EfficientNet is current.

Quality

EfficientNet has 0 bugs and 0 code smells.

Security

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

EfficientNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

EfficientNet 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.

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

EfficientNet 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.

EfficientNet saves you 262 person hours of effort in developing the same functionality from scratch.

It has 635 lines of code, 26 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Run the main function .
Parse command line arguments .
Initialize the model .
Create train MXNet s rec pipeline .
Get the coefficients for a given model .
Convenience function to create an elasticsearch model .
Define the graph .
Add convolution layer .
Forward forward computation .

Get all kandi verified functions for this library.

EfficientNet Key Features

No Key Features are available at this moment for EfficientNet.

EfficientNet Examples and Code Snippets

No Code Snippets are available at this moment for EfficientNet.

Community Discussions

Trending Discussions on EfficientNet

Is there a way to upscale an image using a layer in a machine learning model?

Loading a pretrained model in PyTorch, error:object not callable

How to clear Colab Tensorflow TPU memory

Unable to Import Efficientnet in Colab

Why is my model giving poor accuracy when the data is loaded using tf.data?

When modifying a pre-trained model in pytorch, does the old weight get re-initialized?

Incorrect Shape in Tensorflow Neural Network - Batch size in shape

Am I mislabeling my data in my neural network?

How to use EfficientNet-Lite4 with tensorflow Lite

Which of the following CNN models are used for which computer vision task?

QUESTION

Is there a way to upscale an image using a layer in a machine learning model?

Asked 2022-Mar-01 at 15:28

For instance, consider I have a (32,32,1) input grayscale image. I want to use EfficientNet or any other pre-trained model to classify the data (or basically use transfer learning of any sort). Since EfficientNet takes a minimum of (75,75,3) sized images, is it possible for to upscale my image using ONLY model weights?

For example, any combination of Conv2D, Dense etc which can work for my use case.

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ANSWER

Answered 2022-Feb-28 at 18:13

You can use tf.keras.layers.Resizing which resizes an image input to a target height and width after the input layer inside your DL model. check the doc for more details.
or
If you read the images data from a folder you don't need to add a new layer to your model. You can use tf.keras.utils.image_dataset_from_directory method and specify image_size (it's just an argument which is the Size to resize images to after they are read from disk) as your desired target size

Source https://stackoverflow.com/questions/71298864

QUESTION

Loading a pretrained model in PyTorch, error:object not callable

Asked 2022-Feb-07 at 21:09

I am trying to load the Efficientnet-b6 weights using PyTorch and Fastai:

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ANSWER

Answered 2022-Feb-07 at 21:09

Given the restrained context, I suspect that the problem resides in model, probably containing an OrderedDict of the EfficientNet model state dict, while the EARUnet expects the EfficientNet nn.Module.

You should instead, try something like:

Source https://stackoverflow.com/questions/71023630

QUESTION

How to clear Colab Tensorflow TPU memory

Asked 2022-Jan-26 at 21:55

I am executing model for several folds. After each fold I want to clear the TPU memory so that I don't get OOM error.

Full trace of the current error.

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ANSWER

Answered 2021-Jul-29 at 22:25

I personally wouldn't try to clear TPU memory. If there is an OOM on a Google Colab TPU, either use a smaller batch size, smaller model, or use a Kaggle TPU which has twice the memory as a Colab TPU.

Source https://stackoverflow.com/questions/68582927

QUESTION

Unable to Import Efficientnet in Colab

Asked 2022-Jan-26 at 06:25

I am working in colab to test a code. While importing models, its giving error No module named 'efficientnet' I am sharing the code and error here.

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ANSWER

Answered 2022-Jan-26 at 06:25

It should be,

Source https://stackoverflow.com/questions/70859188

QUESTION

Why is my model giving poor accuracy when the data is loaded using tf.data?

Asked 2021-Dec-26 at 10:22

I am new to the tf.data API and trying to use it to load images from disk in the Dogs vs. Cats Redux: Kernels Edition Kaggle competition. To do this, I first created a pandas DataFrame named train_df with two columns - file_path containing the relative path of images and target containing the target labels 0 (for cat) and 1(for dog). Here's how the first 10 rows of the DataFrame looks like:

Then, I tried loading the images with the following code:

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ANSWER

Answered 2021-Dec-26 at 10:22

In the above notebook, change the input reading function read_images as follows:

Source https://stackoverflow.com/questions/70481895

QUESTION

When modifying a pre-trained model in pytorch, does the old weight get re-initialized?

Asked 2021-Nov-02 at 00:02

I'm modifying a pretrained efficient-net model in pytorch. I'm doing the following in order:

Create the default model, load the imagenet weights.
Then, change the number of channels in the first layer, and delete few layers while adding few.

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ANSWER

Answered 2021-Jul-27 at 05:44

If you are re-defining some of the layers, which you seem to be doing with model._conv_stem and model._fc. Then, yes those will be randomly initialized, thus meaning the loaded weights will no longer be used on those layers. The rest of the model will of course stay untouched and will use the loaded weights.

Source https://stackoverflow.com/questions/68537629

QUESTION

Incorrect Shape in Tensorflow Neural Network - Batch size in shape

Asked 2021-Oct-19 at 11:36

I'm following this tutorial on implementing EfficientNet on Keras.:

Tensorflow

I am using my own dataset and so had to load it manually. For some reason the batch size is being included in the tensor shape and it is throwing an error.

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ANSWER

Answered 2021-Oct-19 at 11:36

As commented by @Frightera, tf.keras.utils.image_dataset_from_directory returns image shape of 4Dimension.

Sample code

Source https://stackoverflow.com/questions/69454679

QUESTION

Am I mislabeling my data in my neural network?

Asked 2021-Oct-07 at 17:03

I'm working on an implementation of EfficientNet in Tensorflow. My model is overfitting and predicting all three classes as just a single class. My training and validation accuracy is in the 99% after a few epochs and my loss is <0.5. I have 32,000 images between the three classes (12, 8, 12).

My hypothesis is that it has to do with the way I input the data and one hot coded the labels. Perhaps it is due to everything being labeled the same accidentally, but I can't figure out where.

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ANSWER

Answered 2021-Oct-07 at 15:56

If you use labels='inferred', you should also specify class_names which will be the name of the three folders you're getting images from.

Source https://stackoverflow.com/questions/69482458

QUESTION

How to use EfficientNet-Lite4 with tensorflow Lite

Asked 2021-Sep-28 at 03:30

I try to use EfficientNet-Lite4 on google colab with tensorflow Lite but i have an error name 'efficienetnet_lite4_spec' is not defined What should I do? Or does it only work with EfficientNet-Lite0?

...

ANSWER

Answered 2021-Sep-28 at 03:30

From comments

Code change from

Source https://stackoverflow.com/questions/69227259

QUESTION

Which of the following CNN models are used for which computer vision task?

Asked 2021-Sep-14 at 15:36

Is my classification correct?

LeNet-5: Image classification,
AlexNet: Image classification,
VGG-16: Image classification,
ResNet: Image classification,
Inception module: Image classification,
MobileNet: Image classification,
EfficientNet: Image classification,
Neural Style Transfer: Image generation,
Sliding Windows Detection algorithm: Object detection,
R-CNN: Object detection,
YOLO: Object detection,
Siamese network: Image recognition,
U-Net: Semantic segmentation

If wrong, please correct me. THANKS!

...

ANSWER

Answered 2021-Sep-14 at 15:36

Your classification is correct if the purpose is - why they were invented initially. However rather than the task based taxonomy, CNNs are better studied on the basis of what different they are doing. Initially CNNs were designed for image classification, but the same network works for Object detection with slight modifications in last layer. For example, Faster RCNN (designed for Object detection) can use any of the architecture designed for classification such as VGG, ResNet etc (link). Similarly Faster-RCNN can be modified to do segmentation task in Mask-RCNN architecture (link).

Here is a chart showing evolutionary history of deep CNNs showing architectural innovations (source)

Here is another taxonomy showing different categories based on architecture style.

Source https://stackoverflow.com/questions/69179967

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

Vulnerabilities

No vulnerabilities reported

Install EfficientNet

You can download it from GitHub.
You can use EfficientNet 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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