gen-efficientnet-pytorch | Pretrained EfficientNet EfficientNet-Lite MixNet | Computer Vision library

by rwightman Python Version: Current License: Apache-2.0

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

kandi X-RAY | gen-efficientnet-pytorch Summary

gen-efficientnet-pytorch is a Python library typically used in Artificial Intelligence, Computer Vision, Pytorch applications. gen-efficientnet-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install gen-efficientnet-pytorch' or download it from GitHub, PyPI.

Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS

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gen-efficientnet-pytorch has a medium active ecosystem.

It has 1529 star(s) with 209 fork(s). There are 45 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 51 have been closed. On average issues are closed in 19 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gen-efficientnet-pytorch is current.

Quality

gen-efficientnet-pytorch has 0 bugs and 0 code smells.

Security

gen-efficientnet-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gen-efficientnet-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gen-efficientnet-pytorch 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

gen-efficientnet-pytorch releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

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

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

gen-efficientnet-pytorch saves you 1475 person hours of effort in developing the same functionality from scratch.

It has 3291 lines of code, 303 functions and 28 files.

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

Top functions reviewed by kandi - BETA

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

Create a data loader
Generate the transformations for an image
Return a PIL interpolation for a given method
Select convolutional convolution
Create a convolution layer
Forward convolutional layer
2d convolutional layer
Perform forward convolution
Drop a tensor
Create a MNIST model
Load a checkpoint
Resolve data from parameters
Generate a tf coefficient network
Generate a tensorflow model
Generates a tf coefficientnet
Generate a tf efficient model
Generate a Lollenetv3 model
Generates a tf coefficientnet network
Computes a tf lossnet model
Compute accuracy
Recursively traverse a graph
Forward the network
Finds images and targets of given types
Perform the forward computation
Forward forward projection
Forward computation

Get all kandi verified functions for this library.

gen-efficientnet-pytorch Key Features

No Key Features are available at this moment for gen-efficientnet-pytorch.

gen-efficientnet-pytorch Examples and Code Snippets

No Code Snippets are available at this moment for gen-efficientnet-pytorch.

Community Discussions

Trending Discussions on gen-efficientnet-pytorch

Can you use a different image size during transfer learning?

How to use Pytorch to create a custom EfficientNet with the last layer written correctly

QUESTION

Can you use a different image size during transfer learning?

Asked 2020-Dec-31 at 10:47

I have made a switch from TensorFlow to PyTorch recently. I use a famous Github repo for training on EfficientNets. I wrote the model initiation class as follows:

...

ANSWER

Answered 2020-Dec-31 at 10:47

Yes you can use different input sizes when it comes to transfer learning, after all the model that you load is just the set of weights of the fixed sequence of layers and fixed convolution kernel sizes. But I believe that there is some sort of minimum size that the model needs to work efficiently. You would still need to re-train the model but it will still converge quite quickly.

You would have to check the official implementation on the minimum size of the model like the one in VGG16 where they specify that the width and height need to be at least 32.

Source https://stackoverflow.com/questions/65516526

QUESTION

How to use Pytorch to create a custom EfficientNet with the last layer written correctly

Asked 2020-Nov-21 at 06:00

I have a classification problem to predict 8 classes for example, I am using EfficientNetB3 in pytorch from here. However, I got confused on whether my custom class is correctly written. I think I want to strip the last layer of the pre-trained model to suit the 8 outputs right? Did I do it correctly? Because when I print y_preds = model(images) in my DataLoader, it seems to give me 1536 predictions. Is this an expected behavior?

...

ANSWER

Answered 2020-Nov-21 at 06:00

You're not even using self.fc in forward pass.

Either just introduce it as:

Source https://stackoverflow.com/questions/64939460

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

Vulnerabilities

No vulnerabilities reported

Install gen-efficientnet-pytorch

You can install using 'pip install gen-efficientnet-pytorch' or download it from GitHub, PyPI.
You can use gen-efficientnet-pytorch 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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