SimCLR | PyTorch implementation of SimCLR | Machine Learning library

by leftthomas Python Version: Current License: No License

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 and it has low support. However SimCLR build file is not available. You can download it from GitHub.

A PyTorch implementation of SimCLR based on ICML 2020 paper A Simple Framework for Contrastive Learning of Visual Representations.

Support

Quality

Security

License

Reuse

Support

SimCLR has a low active ecosystem.

It has 462 star(s) with 99 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 33 have been closed. On average issues are closed in 10 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SimCLR is current.

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 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

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

SimCLR 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.

SimCLR saves you 86 person hours of effort in developing the same functionality from scratch.

It has 222 lines of code, 8 functions and 4 files.

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

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.

Train the loss function
Train the network
Run the test
Train the model

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 :

...

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

You can download it from GitHub.
You can use SimCLR 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 .

Find more information at: