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# PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations # PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
![Image of SimCLR Arch](https://sthalles.github.io/assets/contrastive-self-supervised/cover.png)
Check out the Blog post with full documentation: [Exploring SimCLR: A Simple Framework for Contrastive Learning of Visual Representations](https://sthalles.github.io/simple-self-supervised-learning/) Check out the Blog post with full documentation: [Exploring SimCLR: A Simple Framework for Contrastive Learning of Visual Representations](https://sthalles.github.io/simple-self-supervised-learning/)
## Config file ## Config file
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Feature evaluation is done using a linear model protocol. Feature are learnt using the ```STL10 unsupervised``` set and evaluated in the train/test splits; Feature evaluation is done using a linear model protocol. Feature are learnt using the ```STL10 unsupervised``` set and evaluated in the train/test splits;
Check the ```feature_eval/FeatureEvaluation.ipynb``` notebook for reproducebility. Check the ```feature_eval/FeatureEvaluation.ipynb``` notebook for reproducebility.
| Feature Extractor | Method | Architecture | Top 1 |
| Feature Extractor | Architecture | Top 1 | |:-------------------:|:------------:|:------------:|:-----:|
|-----------------------|--------------|-------| | Logistic Regression | PCA Features | - | 36.0% |
| Using PCA Features | | | | KNN | PCA Features | - | 31.8 |
| Logistic Regression | - | 36.0% | | Logistic Regression | SimCLR | ResNet-18 | 71.8% |
| KNN | - | 31.8 | | KNN | SimCLR | ResNet-18 | 66.7% |
| Using SimCLR Features | | |
| Logistic Regression | ResNet-18 | 71.8% |
| KNN | ResNet-18 | 66.7% |