SimCLR/README.md

PyTorch 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 Tensorflow 2.0 Implementation: Tensorflow SimCLR

Instation

$ conda create --name simclr python=3.7 --file requirements.txt
$ conda activate simclr
$ python run.py

Config file

Before running SimCLR, make sure you choose the correct running configurations on the config.yaml file.

# A batch size of N, produces 2 * (N-1) negative samples. Original implementation uses a batch size of 8192
batch_size: 512 

# Number of epochs to train
epochs: 40

# Frequency to eval the similary score using the validation set
eval_every_n_epochs: 1

# Specify a folder containing a pre-trained model to fine-tune
fine_tune_from: 'Mar13_20-12-09_thallessilva'

# Frequency to which tensorboard is updated
log_every_n_steps: 50

# Model related parameters
model:
  # Output dimensionality of the embedding vector z. Original implementation uses 2048
  out_dim: 256 
  
  # The ConvNet base model. Choose one of: "resnet18" or "resnet50". Original implementation uses resnet50
  base_model: "resnet18"

# Dataset related parameters
dataset:
  s: 1
  
  # dataset input shape. For datasets containing images of different size, this defines the final 
  input_shape: (96,96,3) 
  
  # Number of workers for the data loader
  num_workers: 0
  
  # Size of the validation set in percentage
  valid_size: 0.05

# NTXent loss related parameters
loss:
  # Temperature parameter for the contrastive objective
  temperature: 0.5 
  
  # Distance metric for contrastive loss. If False, uses dot product. Original implementation uses cosine similarity.
  use_cosine_similarity: True

Feature Evaluation

Feature evaluation is done using a linear model protocol.

Features are learned using the STL10 train+unsupervised set and evaluated in the test set;

Check the feature_eval/linear_feature_eval.ipynb notebook for reproducebility.

Feature Extractor	Method	Architecture	Top 1
Logistic Regression	PCA Features	-	36.0%
KNN	PCA Features	-	31.8%
Logistic Regression	SimCLR	ResNet-18	75.0%
KNN	SimCLR	ResNet-18	70.0%

Download pre-trained model

• ResNet-18 Trained using STl10 unsupervised set.