simclr | Big Self-Supervised Models | Machine Learning library

 by   google-research Jupyter Notebook Version: 1.0 License: Apache-2.0

kandi X-RAY | simclr Summary

kandi X-RAY | simclr Summary

simclr is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning applications. simclr has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

News! We have released a TF2 implementation of SimCLR (along with converted checkpoints in TF2), they are in tf2/ folder.

            kandi-support Support

              simclr has a medium active ecosystem.
              It has 3556 star(s) with 570 fork(s). There are 46 watchers for this library.
              It had no major release in the last 12 months.
              There are 62 open issues and 133 have been closed. On average issues are closed in 28 days. There are 7 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of simclr is 1.0

            kandi-Quality Quality

              simclr has 0 bugs and 0 code smells.

            kandi-Security 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.

            kandi-License License

              simclr 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.

            kandi-Reuse Reuse

              simclr releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              It has 1366 lines of code, 71 functions and 8 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

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            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


            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"( 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 ?



            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).



            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 :



            Answered 2021-Apr-01 at 08:30

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


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


            No vulnerabilities reported

            Install simclr

            Our models are trained with TPUs. It is recommended to run distributed training with TPUs when using our code for pretraining. Our code can also run on a single GPU. It does not support multi-GPUs, for reasons such as global BatchNorm and contrastive loss across cores. The code is compatible with both TensorFlow v1 and v2. See requirements.txt for all prerequisites, and you can also install them using the following command.


            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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            gh repo clone google-research/simclr

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