September Virtual Computer Vision Meetup
September 14, 10AM PDT (17:00 UTC)
Sept. 14, 2023 – 10 AM PDT (1:00 PM EDT / 17:00 UTC)
Virtual / Zoom
- ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation
- From Model to the Edge, Putting Your Model into Production
- Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS
- Closing Remarks
ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation
Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.
Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.
Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.
Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah
From Model to the Edge, Putting Your Model into Production
This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.
Speaker: Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.
Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS
In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.
Speaker: Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.
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