pytorch-byol's Introduction

PyTorch-BYOL

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

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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