cutmix | use PyTorch Extension of Unofficial CutMix Implementations | Machine Learning library

by ildoonet Python Version: v0.1 License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | cutmix Summary

cutmix is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. cutmix 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 Ready-to-use PyTorch Extension of Unofficial CutMix Implementations. This re-implementation is improved in some parts,. Hence, there may be slightly-improved training results also.

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Security

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Support

cutmix has a low active ecosystem.

It has 155 star(s) with 27 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 3 have been closed. On average issues are closed in 40 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cutmix is v0.1

Quality

cutmix has 0 bugs and 0 code smells.

Security

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

cutmix code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cutmix is licensed under the MIT License. This license is Permissive.

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

Reuse

cutmix 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, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Autocomplete for autaug
Create an integer parameter
Return a floating point parameter
Get an augment by name
Run one epoch
Compute accuracy
Backward computation
Get learning rate from optimizer
Compute cross entropy between target and target
Calculate the cross entropy of input and target
Decorator to remove duplicate policies
Removes duplicates from a list of policies
Cut out an image
Cut out an absolute value
Demo reduced image
List of AutoAugmentPolicy
Augment list
Adjust the learning rate for optimizer
Save checkpoint
Adjust the learning rate
Adjust resnet based on resnet scheduler scheduler
Autocomplete for the paper
Reduced cifar10

Get all kandi verified functions for this library.

cutmix Key Features

No Key Features are available at this moment for cutmix.

cutmix Examples and Code Snippets

No Code Snippets are available at this moment for cutmix.

Community Discussions

Trending Discussions on cutmix

Image normalization by tf.image.convert_image_dtype function

tf slices with lambda layer only uses last index

Does creating tensors in the call method of a custom layer create new nodes on the graph each time the custom layer is called?

OpenCV DNN throwing exception in case of PNG images

QUESTION

Image normalization by tf.image.convert_image_dtype function

Asked 2022-Mar-25 at 17:20

According to documentation tf.image.convert_image_dtype "Images that are represented using floating point values are expected to have values in the range [0,1)."

But in the keras tutorial(https://keras.io/examples/vision/cutmix/) i have seen the following preprocessing function:

...

ANSWER

Answered 2022-Mar-25 at 17:18

When using convert_image_dtype(image, tf.float32) only type of number in image convert to float32 and don't place [0,1) but when you divide by 255.0 you move number to [0,1) and we do this for Convolutional Layers.

Source https://stackoverflow.com/questions/71619495

QUESTION

tf slices with lambda layer only uses last index

Asked 2021-Sep-01 at 15:13

TLDR

My for lambda layers to get tensor slices only get the last column of data.

I have a (Batch_size, R) shape tensor that I will be running through an embedding layer for each of the R features seperately. I wrote the following code to split the input (Batch_size, R) shaped tensor into R (None,) slices.

...

ANSWER

Answered 2021-Sep-01 at 15:13

The reason why your first block of codes not working is you need to write the lambda function like this instead: lambda a,k=i: a[:,k]

Source https://stackoverflow.com/questions/69016083

QUESTION

Does creating tensors in the call method of a custom layer create new nodes on the graph each time the custom layer is called?

Asked 2021-Aug-16 at 08:36

TLDR

I need random effects at each call of my custom layer. If I create a random mask (and also create a new tensor by shuffling the input data) in the call method instead of the init or build methods, will this create new nodes on the computation graph with every function call?

I am creating a custom CutMix-like layer to use on tabular data. This layer will 1) take a minibatch, 2) create a shuffled version of the original minibatch, 3) replace the values of the original for the shuffled at bernouilli(p). This is often referred to as SwapNoise on Kaggle.

This layer relies on a random mask (drawn bernoulli(p)) to switch out the original values for the shuffled values. In the official custom layer guide, I see new layers inside a custom layer in either the init or the build methods of the class. As my layer needs a unique random mask at each minibatch, I have placed the mask generation in the call method of the class. The code is below:

...

ANSWER

Answered 2021-Aug-16 at 08:36

I need random effects at each call of my custom layer. If I create a random mask (and also create a new tensor by shuffling the input data) in the call method instead of the init or build methods, will this create new nodes on the computation graph with every function call?

Doing this, i.e., msk = tf.keras.backend.random_bernoulli(inputs.shape, p=1 - self.noise, dtype=tf.float32) in the call method will not create new nodes in the graph each time the model is called.

From their source codes:

Source https://stackoverflow.com/questions/68771965

QUESTION

OpenCV DNN throwing exception in case of PNG images

Asked 2021-Jan-12 at 08:30

I am pretty new to computer vision and OpenCv, Python, yolo. I tried to build an inference layer that detects the objects in images and videos. It works smoothly for JPEGs and Videos but as soon as I tried to run the system on PNG it throws

...

ANSWER

Answered 2021-Jan-12 at 08:30

It seems the module you are using only works with images with 3 channels (RGB) but PNG images have 4 channels (RGB + Alpha), you can convert your input image manually.

Source https://stackoverflow.com/questions/65680234

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

Vulnerabilities

No vulnerabilities reported

Install cutmix

This repository is pip-installable,. or you can copy 'cutmix' folder to your project to use it.

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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