# BYOL¶

## 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.

## Results and Models¶

In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models are pre-trained on ImageNet-1k dataset.

### Classification¶

The classification benchmarks includes 4 downstream task datasets, VOC, ImageNet, iNaturalist2018 and Places205. If not specified, the results are Top-1 (%).

#### VOC SVM / Low-shot SVM¶

The Best Layer indicates that the best results are obtained from which layers feature map. For example, if the Best Layer is feature3, its best result is obtained from the second stage of ResNet (1 for stem layer, 2-5 for 4 stage layers).

Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.

Self-Supervised Config Best Layer SVM k=1 k=2 k=4 k=8 k=16 k=32 k=64 k=96
resnet50_8xb32-accum16-coslr-200e feature5 86.31 45.37 56.83 68.47 74.12 78.30 81.53 83.56 84.73

#### ImageNet Linear Evaluation¶

The Feature1 - Feature5 don’t have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to resnet50_mhead_linear-8xb32-steplr-90e_in1k for details of config.

The AvgPool result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to resnet50_linear-8xb512-coslr-90e_in1k for details of config.

Self-Supervised Config Feature1 Feature2 Feature3 Feature4 Feature5 AvgPool
resnet50_8xb32-accum16-coslr-200e 15.16 35.26 47.77 63.10 71.21 71.72
resnet50_16xb256-coslr-200e 15.41 35.15 47.77 62.59 71.85 71.88

#### Places205 Linear Evaluation¶

The Feature1 - Feature5 don’t have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to resnet50_mhead_8xb32-steplr-28e_places205.py for details of config.

Self-Supervised Config Feature1 Feature2 Feature3 Feature4 Feature5
resnet50_8xb32-accum16-coslr-200e 21.25 36.55 43.66 50.74 53.82
resnet50_8xb32-accum16-coslr-300e 21.18 36.68 43.42 51.04 54.06

#### ImageNet Nearest-Neighbor Classification¶

The results are obtained from the features after GlobalAveragePooling. Here, k=10 to 200 indicates different number of nearest neighbors.

Self-Supervised Config k=10 k=20 k=100 k=200
resnet50_8xb32-accum16-coslr-200e 63.9 64.2 62.9 61.9
resnet50_8xb32-accum16-coslr-300e 66.1 66.3 65.2 64.4

### Detection¶

The detection benchmarks includes 2 downstream task datasets, Pascal VOC 2007 + 2012 and COCO2017. This benchmark follows the evluation protocols set up by MoCo.

#### Pascal VOC 2007 + 2012¶

Please refer to faster_rcnn_r50_c4_mstrain_24k_voc0712.py for details of config.

Self-Supervised Config AP50
resnet50_8xb32-accum16-coslr-200e 80.35

#### COCO2017¶

resnet50_8xb32-accum16-coslr-200e 40.9 61.0 44.6 36.8 58.1 39.5

### Segmentation¶

The segmentation benchmarks includes 2 downstream task datasets, Cityscapes and Pascal VOC 2012 + Aug. It follows the evluation protocols set up by MMSegmentation.

#### Pascal VOC 2012 + Aug¶

Please refer to fcn_r50-d8_512x512_20k_voc12aug.py for details of config.

Self-Supervised Config mIOU
resnet50_8xb32-accum16-coslr-200e 67.16

## 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}
}