SimCLR | PyTorch implementation of SimCLR : A Simple Framework | Machine Learning library

by Spijkervet Python Version: 1.2 License: MIT

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

kandi X-RAY | SimCLR Summary

SimCLR is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. SimCLR 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.

SimCLR is a "simple framework for contrastive learning of visual representations". The contrastive prediction task is defined on pairs of augmented examples, resulting in 2N examples per minibatch. Two augmented versions of an image are considered as a correlated, "positive" pair (x_i and x_j). The remaining 2(N - 1) augmented examples are considered negative examples. The contrastive prediction task aims to identify x_j in the set of negative examples for a given x_i.

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SimCLR has a low active ecosystem.

It has 611 star(s) with 142 fork(s). There are 9 watchers for this library.

It had no major release in the last 12 months.

There are 8 open issues and 26 have been closed. On average issues are closed in 91 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of SimCLR is 1.2

Quality

SimCLR has 0 bugs and 0 code smells.

Security

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

SimCLR code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SimCLR 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

SimCLR releases are available to install and integrate.

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

Patch the replication callback
Execute the replication callbacks
Create a slave pipe
Internal helper for _data_parallel
Forward computation
Get the result from the queue
Run the given message in the queue
Persist the result
Train the model
Gradient of gradients
Performs the training step
Compute the loss function
Performs the data partition on the given list of intermediates
Compute the mean and bias for a batch norm
Performs a single step
Wrapper for inference
Save the state of the checkpoint
Get a ResNet version from the given name
Load the optimizer
Create data loader from arrays
Run the build
Test function
Load yaml config file
Convert a PyTorch model into a module
Resets the parameters

Get all kandi verified functions for this library.

SimCLR Key Features

No Key Features are available at this moment for SimCLR.

SimCLR Examples and Code Snippets

No Code Snippets are available at this moment for SimCLR.

Community Discussions

Trending Discussions on SimCLR

How to use K means clustering to visualise learnt features of a CNN model?

using tfds for using my custom dataset with tensorflow fails

QUESTION

How to use K means clustering to visualise learnt features of a CNN model?

Asked 2021-Oct-19 at 14:42

Recently I was going through the paper : "Intriguing Properties of Contrastive Losses"(https://arxiv.org/abs/2011.02803). In the paper(section 3.2) the authors try to determine how well the SimCLR framework has allowed the ResNet50 Model to learn good quality/generalised features that exhibit hierarchical properties. To achieve this, they make use of K-means on intermediate features of the ResNet50 model (intermediate means o/p of block 2,3,4..) & quote the reason -> "If the model learns good representations then regions of similar objects should be grouped together".

Final Results : KMeans feature visualisation

I am trying to replicate the same procedure but with a different model (like VggNet, Xception), are there any resources explaining how to perform such visualisations ?

...

ANSWER

Answered 2021-Oct-19 at 14:42

The procedure would be as follow:

Let us assume that you want to visualize the 8th layer from VGG. This layer's output might have the shape (64, 64, 256) (I just took some random numbers, this does not correspond to actual VGG). This means that you have 4096 256-dimensional vectors (for one specific image). Now you can apply K-Means on these vectors (for example with 5 clusters) and then color your image corresponding to the clustering result. The coloring is easy, since the 64x64 feature map represents a scaled down version of your image, and thus you just color the corresponding image region for each of these vectors.

I don't know if it might be a good idea to do the K-Means clustering on the combined output of many images, theoretically doing it on many images and one a single one should both give good results (even though for many images you probably would increase the number of clusters to account for the higher variation in your feature vectors).

Source https://stackoverflow.com/questions/69632019

QUESTION

using tfds for using my custom dataset with tensorflow fails

Asked 2021-Apr-01 at 08:30

according to the tutorial at this link I want to create my custom dataset and use it with tensorflow.

I have installed the tfds command and when I entering tfds new my_dataset command, I will encounter to this error :

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ANSWER

Answered 2021-Apr-01 at 08:30

this problem solved in tensorflow V2. so use tensorflow V2 or higher versions.

Source https://stackoverflow.com/questions/66857077

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

Vulnerabilities

No vulnerabilities reported

Install SimCLR

This downloads a pre-trained model and trains the linear classifier, which should receive an accuracy of ±82.9% on the STL-10 test set.

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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