Singapore AI, Machine Learning and Computer Vision Meetup

https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-oct-24-2024/

Accelerating Machine Learning Research and Development for Autonomy

At Oxa (Autonomous Vehicle Software), we designed an automated workflow for building machine vision models at scale from data collection to in-vehicle deployment, involving a number of steps, such as, intelligent route planning to maximise visual diversity; sampling of the sensor data w.r.t. visual and semantic uniqueness; language-driven automated annotation tools and multi-modal search engine; and sensor data expansion using generative methods.

About the Speaker

Guillaume Rochette is a Staff Engineer at Oxa MetaDriver, a suite of tools that combines generative AI, digital twins and simulation to accelerate machine learning and testing of self-driving technology before and during real-world use. Prior to that, he did a PhD. in Machine Vision at the University of Surrey on “Pose Estimation and Novel View Synthesis of Humans”. He is currently working on Machine Vision and 3D Geometric Understanding for autonomous driving.

Pixels Are All You Need: Utilizing 2D Image Representations in Applied Robotics

Many vision-based robot control applications (like those in manufacturing) require 3D estimates of task-relevant objects, which can be realized by training a direct 3D object detection model. However, obtaining 3D annotation for a specific application is expensive relative to 2D object representations like segmentation masks or bounding boxes.
In this talk, Brent will describe how we achieve mobile robot manipulation using inexpensive pixel-based object representations combined with known 3D environmental constraints and robot kinematics. He will also discuss how recent Visual AI developments show promise to further reduce the cost of 2D training data, thereby increasing the practicality of pixel-based objects representations in robotics.

About the Speaker

Brent Griffin, PhD is a Principal Machine Learning Scientist at Voxel51. Previously, he was the Perception Lead at Agility Robotics and an assistant research scientist at the University of Michigan conducting research at the intersection of computer vision, control, and robot learning. He is lead author on publications in all of the top IEEE conferences for computer vision, robotics, and control, and his work has been featured in Popular Science, in IEEE Spectrum, and on the Big Ten Network.

https://voxel51.com/computer-vision-events/getting-started-with-fiftyone-workshop-oct-30-2024/

About the workshop

Want greater visibility into the quality of your computer vision datasets and models? Then join Harpreet Sahota, Hacker in Residence and Machine Learning Engineer at Voxel51, for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset.

In the first part of the workshop we’ll cover:

• FiftyOne Basics (terms, architecture, installation, and general usage)
• An overview of useful workflows to explore, understand, and curate your data
• How FiftyOne represents and semantically slices unstructured computer vision data

The second half will be a hands-on introduction to FiftyOne, where you will learn how to:

• Load datasets from the FiftyOne Dataset Zoo
• Navigate the FiftyOne App
• Programmatically inspect attributes of a dataset
• Add new sample and custom attributes to a dataset
• Generate and evaluate model predictions
• Save insightful views into the data

Prerequisites are a working knowledge of Python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop.

About the Instructor

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

https://voxel51.com/computer-vision-events/developing-fiftyone-plugins-workshop-nov-6-2024/

Are you ready to take your computer vision tooling to the next level? Open source FiftyOne is the most flexible computer vision toolkit on the planet. By tapping into its built-in FiftyOne Plugin framework, you can extend your FiftyOne experience and streamline your workflows, building Gradio-like applications with data at their core.

From concept interpolation to image deduplication, optical character recognition, and even curating your own AI art gallery by adding generated images directly into a dataset, your imagination is the only limit. Join us to discover how you can unleash your creativity and interact with data like never before.

The FREE workshop will cover:

• FiftyOne Plugins – what are they?
• Installing a plugin
• Creating your own Python plugin
• Curate your own AI art gallery with DALLE3, SDXL, and Latent Consistency Models
• Bring GPT4 Vision directly to your data
• Run OCR, semantic document searches, and make your textual data visual
• Create an open-vocabulary labeling interface

Prerequisites

A working knowledge of Python and basic familiarity with FiftyOne. All attendees will get access to the tutorials, videos, and code examples used in the workshop.

About the Instructor

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes.

https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-nov-14-2024/

Human-in-the-loop: Practical Lessons for Building Comprehensive AI Systems

AI systems often struggle with data limitations, data distribution shift over time, and a poor user experience. Human-in-the-loop design offers a solution by placing users at the center of AI systems and leveraging human feedback for continuous improvement.

In this talk, we'll dive deeply into a recent project at Merantix Momentum: A interactive tool for automatic rodent behaviour analysis in videos at a large scale. We'll discuss the machine learning components, including pose estimation, behavior classification, and active learning and talk about the technical challenges and the impact of the project.

About the Speaker

Adrian Loy has a Msc in IT Systems Engineering and spent the last 5 years at Merantix Momentum planning and executing Computer Vision Projects for a variety of clients. He is currently leading the Machine Learning Engineering Team at Momentum.

Deploying ML models on Edge Devices using Qualcomm AI Hub

In this talk we address the common challenges faced by developers migrating AI workloads from the cloud to edge devices. Qualcomm aims to democratize AI at the edge, easing the transition to the edge by supporting familiar frameworks and data types. ​This is where Qualcomm AI Hub comes in. Developers can follow along, gaining knowledge and tools to efficiently deploy optimized models on real devices using Qualcomm AI Hub. ​

We’ll walk through how to get started using Qualcomm AI Hub, go through examples on how to optimize models and bundle the downloadable target asset into your application and talk through iterating on your model and meet performance requirements to deploy on device!

About the Speaker

Bhushan Sonawane has optimized and deployed more than 1000s of AI models on-device on iOS and Android ecosystem. Currently, he is building AI Hub at Qualcomm to make on-device journey on Android and Snapdragon platform as seamless as possible.

Curating Excellence: Strategies for Optimizing Visual AI Datasets

In this talk Harpreet will discuss common challenges plaguing visual AI datasets, their impact on model performance, and share some tips and tricks for curating datasets to make the most of any compute budget or network architecture.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.