Presentation
HARVEST-2.0: High-Performance Vision Framework for End-to-End Preprocessing, Training, Inference, and Visualization
DescriptionDeep learning (DL) thrives on data; however, it 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-vision 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 labeled data samples. HARVEST-2.0 provides an intuitive web-based interface, enabling domain experts with no prior DL or HPC knowledge to preprocess data, geotag images, train DL models on HPC systems, perform inference, and visualize the results. Our evaluations demonstrate accuracies within 3\% compared 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 preprocessing, interactive labeling, inference, and distributed training in a user-friendly and flexible manner.
Authors
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX
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