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UID:submissions.supercomputing.org_SC24_sess533_post235@linklings.com
SUMMARY:HARVEST-2.0: High-Performance Vision Framework for End-to-End Prep
 rocessing, Training, Inference, and Visualization
DESCRIPTION:Nawras Alnaasan, Anirudh Potlapally, Tian Chen, Matthew Lieber
 , Aamir Shafi, Hari Subramoni, Scott Shearer, and Dhabaleswar K. Panda (Th
 e Ohio State University)\n\nDeep learning (DL) thrives on data; however, i
 t inherits a major limitation: training and testing datasets must be fully
  annotated for supervised deep neural networks (DNNs) training. To address
  this challenge, we introduce HARVEST-2.0, a high-performance computer-vis
 ion framework for end-to-end data preprocessing, training, inference, and 
 visualization of computer vision tasks. HARVEST-2.0 utilizes cutting-edge 
 semi-supervised learning algorithms requiring only a small subset of label
 ed data samples. HARVEST-2.0 provides an intuitive web-based interface, en
 abling domain experts with no prior DL or HPC knowledge to preprocess data
 , geotag images, train DL models on HPC systems, perform inference, and vi
 sualize the results. Our evaluations demonstrate accuracies within 3\% com
 pared to fully supervised training, utilizing less than 80 labeled samples
  per class, and near-linearly reducing the execution time. HARVEST-2.0 is 
 an effort along AI democratization, enabling end-users to carry out prepro
 cessing, interactive labeling, inference, and distributed training in a us
 er-friendly and flexible manner.\n\nRegistration Category: Tech Program Re
 g Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedrich-Alexa
 nder University, Erlangen-Nuremberg; Erlangen National High Performance Co
 mputing Center); Sally Ellingson (University of Kentucky); and Alan Sussma
 n (University of Maryland)\n\n
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