A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training

This repository is the official implementation of A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training.

This repository contains the code to generate synthetic images, the results for reproducing the plots, and pre-trained models.

Generation of Synthetic Images

To generate synthetic images/annotations we used in the experiments, see rendering directory.

Results

To check the accuracy scores of various pre-training/fine-tuning task pairs, see results directory.

Pre-trained Models

We provide pre-trained backbone networks used in the paper. Here, task means the pre-training task, data means the pre-training dataset (see rendering for more details), and # of examples means the size of the dataset. All the pre-trained models are compatible with resnetxx of torchvision.

task	data	# of examples	backbone	download
object detection	bop	64k	ResNet50	N/A
multiclass classification	bop	64k	ResNet50	N/A
surface normal estimation	bop	64k	ResNet50	N/A
semantic segmentation	bop	64k	ResNet50	N/A

Due to the filesize, we cannot put the download links here. If you are interested in, please let me know.

Citation

If you cite our work, please use the following bibtex entry.

@article{mikami2021a,
    title={A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training},
    author={Hiroaki Mikami and Kenji Fukumizu and Shogo Murai and Shuji Suzuki and Yuta Kikuchi and Taiji Suzuki and Shin-ichi Maeda and Kohei Hayashi},
    year={2021},
    journal={arXiv preprint arXiv:2108.11018}
}