TensorFlow implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
Blog post with full documentation: Exploring SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
For a Pytorch Implementation: PyTorch SimCLR
- tensorflow 2.x
Before running SimCLR, make sure you choose the correct running configurations on the config.yaml file.
batch_size: 256 # A batch size of N, produces 2 * (N-1) negative samples. Original implementation uses a batch size of 8192 out_dim: 64 # Output dimensionality of the embedding vector z. Original implementation uses 2048 s: 1 temperature: 0.5 # Temperature parameter for the contrastive objective base_convnet: "resnet18" # The ConvNet base model. Choose one of: "resnet18 or resnet50". Original implementation uses resnet50 use_cosine_similarity: True # Distance metric for contrastive loss. If False, uses dot product epochs: 40 # Number of epochs to train num_workers: 4 # Number of workers for the data loaderFeature evaluation is done using a linear model protocol. Feature are learned using the STL10 unsupervised set and evaluated in the train/test splits;
Check the feature_eval/FeatureEvaluation.ipynb notebook for reproducebility.
| Feature Extractor | Method | Architecture | Top 1 |
|---|---|---|---|
| Logistic Regression | PCA Features | - | - |
| KNN | PCA Features | - | - |
| Logistic Regression | SimCLR | ResNet-18 | - |
| KNN | SimCLR | ResNet-18 | - |