PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning.
Clone the repository and run
$ conda env create --name byol --file env.yml
$ conda activate byol
$ python main.py
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: 0.0004We 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% |