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SimCLR

Pytorch implementation of the paper A Simple Framework for Contrastive Learning of Visual Representations

  • ADAM optimizer
  • ExponentialLR schedular. No warmup or other exotics
  • Batchsize of 256 via gradient accumulation

Feature model

  • Resnet50, where the first convolutional layer has a filter size of 3 instead of 7.
  • h() feature dimensionality: 2048
  • z() learning head output dimensionality: 128

Classifier model

  • Simple 1 layer Neural network from 2048 to num_classes

Classification Results

Epochs 100 200
Paper 83.9 89.2
This repo 87.49 88.16

Run

Train the feature extracting model (resnet). Note CIFAR10C inherits from datasets.CIFAR and provides the augmented image pairs.

python train_features.py --batch-size=64 --accumulation-steps=4 --tau=0.5 
                          --feature-size=128 --dataset-name=CIFAR10C --data-dir=path/to/your/data

Train the classifier model. Needs a saved feature model to extract features from images.

python train_classifier.py --load-model=models/modelname_timestamp.pt --dataset-name=CIFAR10 
                          --data-dir=path/to/your/data