Bangalore AI, Machine Learning and Computer Vision Meetup

2025年10月08日 21:30 IST

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オンライン

Visual AI in Manufacturing: How Multimodal Data Powers Adaptive Process Control

オンライン

165 参加者が44 グループから参加しています

Join us for a fast-paced session that reveals how a data-centric visual AI workflow turns disparate sensor streams into a single, actionable source of truth on the factory floor.

When and Where

Oct 8, 2025
9:00-10:30 AM Pacific
Online: Register for the Zoom

Through live demos and real-world case studies, you’ll see how fusing images, video, thermal, and PLC data accelerates defect detection, drives adaptive process control, and boosts product yield.

What you’ll learn

• Building a multimodal dataset: ingesting images, video, and industrial sensor logs into a unified visual dataset
• Curating for quality: rapid exploration, filtering, and labeling techniques that surface the edge-cases traditional QA misses
• Training & evaluating models: fuse visual and non-visual features into one training and evaluation pipeline, track every experiment, and mine scenarios that contribute to model failures
• Closing the loop: stream model outputs back to the line to trigger real-time corrective actions
• Measuring model impact: align model performance with key metrics like scrap rate, OEE, and MTTR

Who should attend

Manufacturing engineers, computer-vision practitioners, data scientists, and operations leaders looking to use visual AI to optimize their production lines.

2025年10月15日 21:30 IST

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オンライン

Oct 15 - Visual AI in Agriculture (Day 1)

オンライン

234 参加者が44 グループから参加しています

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time
Oct 15 at 9 AM Pacific

Location
Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans.
For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere, where he develops deep learning models for LiDAR and RGB perception in safety-critical, real-time systems. He earned his Ph.D. in Computer Science from Auburn University, with a dissertation on improving computer vision and spatiotemporal deep neural networks, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind, Google, Meta, Microsoft, and OpenAI, among others, and his (batter|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr, and his GitHub repositories—which have collectively received over 2,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

2025年10月16日 21:30 IST

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オンライン

Oct 16 - Visual AI in Agriculture (Day 2)

オンライン

200 参加者が44 グループから参加しています

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time
Oct 16 at 9 AM Pacific

Location
Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline;
(2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets
- Curate training data for improved performance
- Train and evaluate pest detection models
- Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

2025年10月30日 21:30 IST

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オンライン

Oct 30 - AI, ML and Computer Vision Meetup

オンライン

161 参加者が44 グループから参加しています

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Oct 30, 2025
9 AM Pacific
Online. Register for the Zoom!

The Agent Factory: Building a Platform for Enterprise-Wide AI Automation

In this talk we will explore what it takes to build an enterprise-ready AI automation platform at scale. The topics covered will include:

• The Scale Challenge: E-commerce environments expose the limitations of single-point AI solutions, which create fragmented ecosystems lacking cohesion and efficient resource sharing across complex, knowledge-based work.
• Root Cause Analysis Success: Flipkart’s initial AI agent transformed business analysis from days-long investigations to near-instantaneous insights, proving the concept while revealing broader platform opportunities.
• Platform Strategy Evolution: Success across Engineering (SDLC, SRE), Operations, and Commerce teams necessitated a unified, multi-tenant platform serving diverse use cases with consistency and operational efficiency.
• Architectural Foundation: Leveraging framework-agnostic design principles we were able to emphasize modularity, which enabled teams to leverage different AI models while maintaining consistent interfaces and scalable infrastructure.
• The “Agent Garden” Vision: Flipkart’s roadmap envisions an internal ecosystem where teams discover, deploy, and contribute AI agents, providing a practical blueprint for scalable AI agent infrastructure development.

About the Speaker

Virender Bhargav at Flipkart is a seasoned engineering leader whose expertise spans business technology integration, enterprise applications, system design/architecture, and building highly scalable systems. With a deep understanding of technology, he has spearheaded teams, modernized technology landscapes, and managed core platform layers and strategic products. With extensive experience driving innovation at companies like Paytm and Flipkart, his contributions have left a lasting impact on the industry.

Scaling Generative Models at Scale with Ray and PyTorch

Generative image models like Stable Diffusion have opened up exciting possibilities for personalization, creativity, and scalable deployment. However, fine-tuning them in production‐grade settings poses challenges: managing compute, hyperparameters, model size, data, and distributed coordination are nontrivial.

In this talk, we’ll dive deep into learning how to fine-tune Stable Diffusion models using Ray Train (with HuggingFace Diffusers), including approaches like DreamBooth and LoRA. We’ll cover what works (and what doesn’t) in scaling out training jobs, handling large data, optimizing for GPU memory and speed, and validating outputs. Attendees will come away with practical insights and patterns they can use to fine-tune generative models in their own work.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems.
Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).
Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Privacy-preserving in Computer Vision through Optics Learning

Cameras are now ubiquitous, powering computer vision systems that assist us in everyday tasks and critical settings such as operating rooms. Yet, their widespread use raises serious privacy concerns: traditional cameras are designed to capture high-resolution images, making it easy to identify sensitive attributes such as faces, nudity, or personal objects. Once acquired, such data can be misused if accessed by adversaries. Existing software-based privacy mechanisms, such as blurring or pixelation, often degrade task performance and leave vulnerabilities in the processing pipeline.

In this talk, we explore an alternative question: how can we preserve privacy before or during image acquisition? By revisiting the image formation model, we show how camera optics themselves can be learned and optimized to acquire images that are unintelligible to humans yet remain useful for downstream vision tasks like action recognition. We will discuss recent approaches to learning camera lenses that intentionally produce privacy-preserving images, blurry and unrecognizable to the human eye, but still effective for machine perception. This paradigm shift opens the door to a new generation of cameras that embed privacy directly into their hardware design.

About the Speaker

Carlos Hinojosa is a Postdoctoral researcher at King Abdullah University of Science and Technology (KAUST) working with Prof. Bernard Ghanem. His research interests span Computer Vision, Machine Learning, AI Safety, and AI for Science. He focuses on developing safe, accurate, and efficient vision systems and machine-learning models that can reliably perceive, understand, and act on information, while ensuring robustness, protecting privacy, and aligning with societal values.

It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data

Can we match vision and language embeddings without any supervision? According to the platonic representation hypothesis, as model and dataset scales increase, distances between corresponding representations are becoming similar in both embedding spaces. Our study demonstrates that pairwise distances are often sufficient to enable unsupervised matching, allowing vision-language correspondences to be discovered without any parallel data.

About the Speaker

Dominik Schnaus is a third-year Ph.D. student in the Computer Vision Group at the Technical University of Munich (TUM), supervised by Daniel Cremers. His research centers on multimodal and self-supervised learning with a special emphasis on understanding similarities across embedding spaces of different modalities.