1 code implementation • 11 Jul 2022 • Salehe Erfanian Ebadi, Saurav Dhakad, Sanjay Vishwakarma, Chunpu Wang, You-Cyuan Jhang, Maciek Chociej, Adam Crespi, Alex Thaman, Sujoy Ganguly
We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a superior pre-training alternative to ImageNet and other large-scale synthetic data counterparts.
1 code implementation • 17 Dec 2021 • Salehe Erfanian Ebadi, You-Cyuan Jhang, Alex Zook, Saurav Dhakad, Adam Crespi, Pete Parisi, Steven Borkman, Jonathan Hogins, Sujoy Ganguly
We found that pre-training a network using synthetic data and fine-tuning on various sizes of real-world data resulted in a keypoint AP increase of $+38. 03$ ($44. 43 \pm 0. 17$ vs. $6. 40$) for few-shot transfer (limited subsets of COCO-person train [2]), and an increase of $+1. 47$ ($63. 47 \pm 0. 19$ vs. $62. 00$) for abundant real data regimes, outperforming models trained with the same real data alone.
no code implementations • 9 Jul 2021 • Steve Borkman, Adam Crespi, Saurav Dhakad, Sujoy Ganguly, Jonathan Hogins, You-Cyuan Jhang, Mohsen Kamalzadeh, Bowen Li, Steven Leal, Pete Parisi, Cesar Romero, Wesley Smith, Alex Thaman, Samuel Warren, Nupur Yadav
We introduce the Unity Perception package which aims to simplify and accelerate the process of generating synthetic datasets for computer vision tasks by offering an easy-to-use and highly customizable toolset.