Singapore AI, Machine Learning and Computer Vision Meetup

https://voxel51.com/computer-vision-events/developing-fiftyone-plugins-workshop-oct-9-2024/

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-oct-10-2024/

How Renault Leveraged Machine Learning to Scale Electric Vehicle Sales

In 2019, Renault sought a scalable solution to estimate the cost of home charging station installations for electric vehicle buyers. A machine learning solution using satellite images and a shortest-path algorithm was developed to automate this process. Despite challenges, the optimized solution was deployed as a cloud-based API, enabling Renault to scale their EV sales from 50,000 in 2019 to over 220,000 in 2022.

About the Speaker

With a PhD in Physics, Vincent Vandenbussche has over a decade of experience deploying scalable machine learning solutions for leading companies like Renault and Chanel. He is also passionate about sharing his expertise through Medium posts and his book, The Regularization Cookbook.

RGB-X Model Development: Exploring Four Channel ML Workflows

Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!

About the Speaker

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. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search

Search no longer has to be a traditional term / frequency-inverse document frequency slog. The current trend of machine learning and models has opened another dimension for search, quite literally. In this talk we’ll cover “classic” search and its inherent limitations, what a model is and how you can use it.

Next, we’ll look at how to perform vector search or hybrid search using images of birds in Elasticsearch, generate embeddings from images and then use the techniques we learned to propose the most probable similar images after uploading our own image. We’ll close the talk with an overview of various enhancements to increase the performance and usability of your searches.

About the Speaker

Justin Castilla is a Senior Developer Advocate at Elastic based in Seattle. His main focus is education and developer empowerment, and enjoys sharing knowledge and learning experiences with everyone.

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.