byol

BYOL

Bootstrap your own latent: A new approach to self-supervised Learning

Abstract

Bootstrap Your Own Latent (BYOL) is a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('resnet50_byol-pre_8xb512-linear-coslr-90e_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('byol_resnet50_16xb256-coslr-200e_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Train/Test Command

Prepare your dataset according to the docs.

Train:

python tools/train.py configs/byol/byol_resnet50_16xb256-coslr-200e_in1k.py

Test:

python tools/test.py configs/byol/benchmarks/resnet50_8xb512-linear-coslr-90e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-7596c6f5.pth

Models and results

Pretrained models

Model	Params (M)	Flops (G)	Config	Download
byol_resnet50_16xb256-coslr-200e_in1k	68.02	4.11	config	model | log

Image Classification on ImageNet-1k

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Config	Download
resnet50_byol-pre_8xb512-linear-coslr-90e_in1k	BYOL	25.56	4.11	71.80	config	model | log

Citation

@inproceedings{grill2020bootstrap,
  title={Bootstrap your own latent: A new approach to self-supervised learning},
  author={Grill, Jean-Bastien and Strub, Florian and Altch{\'e}, Florent and Tallec, Corentin and Richemond, Pierre H and Buchatskaya, Elena and Doersch, Carl and Pires, Bernardo Avila and Guo, Zhaohan Daniel and Azar, Mohammad Gheshlaghi and others},
  booktitle={NeurIPS},
  year={2020}
}