CauchyFood / PyTorch-BYOL

PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

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PyTorch-BYOL

PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning.

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Installation

Clone the repository and run

$ conda env create --name byol --file env.yml
$ conda activate byol
$ python main.py

Config

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

network:
  name: resnet18 # base encoder. choose one of resnet18 or resnet50
   
  # Specify a folder containing a pre-trained model to fine-tune. If training from scratch, pass None.
  fine_tune_from: 'resnet-18_40-epochs'
   
  # configurations for the projection and prediction heads
  projection_head: 
    mlp_hidden_size: 512 # Original implementation uses 4096
    projection_size: 128 # Original implementation uses 256

data_transforms:
  s: 1
  input_shape: (96,96,3)

trainer:
  batch_size: 64 # Original implementation uses 4096
  m: 0.996 # momentum update
  checkpoint_interval: 5000
  max_epochs: 40 # Original implementation uses 1000
  num_workers: 4 # number of worker for the data loader

optimizer:
  params:
    lr: 0.03
    momentum: 0.9
    weight_decay: 0.0004

Feature Evaluation

We measure the quality of the learned representations by linear separability.

During training, BYOL learns features using the STL10 train+unsupervised set and evaluates in the held-out test set.

Linear Classifier Feature Extractor Architecture Feature dim Projection Head dim Epochs Batch Size STL10 Top 1
Logistic Regression PCA Features - 256 - - 36.0%
KNN PCA Features - 256 - - 31.8%
Logistic Regression (Adam) BYOL (SGD) ResNet-18 512 128 40 64 70.1%
Logistic Regression (Adam) BYOL (SGD) ResNet-18 512 128 80 64 75.2%

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PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning


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