Algorithms

“High-fidelity Conversion of Floating-point Networks for Low-precision Inference Using Distillation with Limited Data,” a Presentation from Imagination Technologies

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James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference Using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit. When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the […]

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“Facing Up to Bias,” a Presentation from Perceive

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Steve Teig, CEO of Perceive, presents the “Facing Up to Bias” tutorial at the May 2021 Embedded Vision Summit. Today’s face recognition networks identify white men correctly more often than white women or non-white people. The use of these models can manifest racism, sexism, and other troubling forms of discrimination. There are also publications suggesting

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“Getting Started with Vision AI Model Training,” a Presentation from NVIDIA

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Ekaterina Sirazitdinova, Data Scientist at NVIDIA, presents the “Getting Started with Vision AI Model Training” tutorial at the May 2021 Embedded Vision Summit. In many modern vision and graphics applications, deep neural networks (DNNs) enable state-of-the-art performance for tasks like image classification, object detection and segmentation, quality enhancement and even new content generation. In this

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“Khronos Group Standards: Powering the Future of Embedded Vision,” a Presentation from the Khronos Group

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Neil Trevett, Vice President of Developer Ecosystems at NVIDIA and President of the Khronos Group, presents the “Khronos Group Standards: Powering the Future of Embedded Vision” tutorial at the May 2021 Embedded Vision Summit. Open standards play an important role in enabling interoperability for faster, easier deployment of vision-based systems. With advances in machine learning,

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“Tools and Strategies for Quickly Building Effective Image Datasets,” a Presentation from BDTI

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Evan Juras, Computer Vision Engineer at BDTI, presents the “Tools and Strategies for Quickly Building Effective Image Datasets,” tutorial at the May 2021 Embedded Vision Summit. A common pain point when using machine learning for computer vision is the need to manually curate and label large quantities of training images. Depending on the application, thousands

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Back to Live: Basler with Innovations at VISION 2021

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As one of the leading international suppliers in the vision technology market, Basler will show what moves the industry forward at the world’s leading trade fair for machine vision: Under the heading “Forget The Problem. See The Solution.” innovative advancements and vision solutions will be presented. Ahrensburg, 24 August 2021 – Basler will be presenting

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“Automated Neural Network Model Training: The Impact on Deploying and Scaling ML at the Edge,” a Presentation from Arm

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Tim Hartley, Vice President of Product and Marketing at SeeChange Technologies (an Arm company), presents the “Automated Neural Network Model Training: The Impact on Deploying and Scaling ML at the Edge” tutorial at the May 2021 Embedded Vision Summit. Neural networks are being used to solve an ever-increasing number of use cases, elevating the importance

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Audio Classification with PyTorch’s Ecosystem Tools

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This blog post was originally published at ClearML’s website. It is reprinted here with the permission of ClearML. Audio classification with torchaudio and ClearML Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis

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“Robust Object Detection Under Dataset Shifts,” a Presentation from Arm

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Partha Maji, Principal Research Scientist at Arm’s Machine Learning Research Lab, presents the “Robust Object Detection Under Dataset Shifts” tutorial at the May 2021 Embedded Vision Summit. In image classification tasks, the evaluation of models’ robustness to increased dataset shifts with a probabilistic framework is very well studied. However, object detection (OD) tasks pose other

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