Montréal AI, Machine Learning and Computer Vision Meetup

Oct 29, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.

By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.

Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.

Nov 05, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Talks will include:

U-Net Framework for Micro-Scale Surface Damage Segmentation in High-Resolution Soybean Seed Imagery

Accurate detection of surface-level seed damage is critical for soybean seed quality assurance, yet automated micro-scale damage detection in high-resolution imagery remains an open challenge due to extreme spatial variability in damage scale, severe class imbalance across damage types, and the computational demands of processing ultra-high resolution agricultural imagery at scale. This research addresses the semantic segmentation of fine-grained soybean seed surface defects, such as wrinkles, dark spots, and general surface damage, in 6048 × 4024 pixel images where target damages can be as small as 18 × 18 pixels, using an augmented dataset of 77,000 individual seed images.

To overcome the difficulties of micro-scale detection, spatial sparsity, and class confusion, we propose a two-stage training and dual model inference framework built on an optimized U-Net architecture with a ResNet34 encoder. In the first stage, a damage specialist model is trained using weighted loss functions and a class-balanced approach that prioritizes damage classes.

In the second stage, transfer learning is applied to initialize a healthy seed specialist model from Stage 1 weights, with rebalanced class weights and tile probabilities that identify healthy seeds. During inference, a confidence-gated damage filter suppresses low-confidence predictions, and healthy seed labels are assigned only when the specialist model's confidence exceeds that of the damage model.

The two specialist models achieve validation accuracies of 94% and 98.53%, respectively, and the combined inference system successfully detects and localizes all three damage categories across unseen test images under conditions of extreme spatial variability and class imbalance. Qualitative evaluation confirms close alignment of predicted boundaries with ground truth annotations across all damage types, including dark spots.

These results demonstrate that confidence-gated dual model inference can reduce class confusion in imbalanced micro-scale segmentation, advancing the feasibility of fine-grained automated seed quality inspection at the scale.

About the Speaker

Saurav Upadhyaya is an AI/ML researcher with a Master's degree in Computer Science, whose work spans agricultural AI, conversational systems, and public health, consistently translating advanced technology into tangible real-world impact.

From camera to drone: wildfire detection and integration at the edge

Wildfires are getting larger and more expensive every year. But caught early, a fire is a small job for a small team; caught late, nothing stops it. The cameras to catch them are already installed, so the work is not necessary more sensors but making the ones we have act.

This talk walks the whole chain: a small vision model running on the camera that flags smoke, an integration layer that scores the alert against terrain, weather and history to decide whether it matters, and a drone dispatched to confirm before anyone commits a crew. We will cover what has to run at the edge and why, how small the model can get before it stops seeing smoke, and what it takes to trust a detection nobody has looked at yet.

About the Speaker

Maxime Carriere is co-founder of Kernwerk in Berlin, where he works on compressing AI models to run on cheap embedded hardware.

5,000 Flights to Answer One Question: Can a Drone Sample as Well as a Human?

Environmental testing still begins with a person walking onto a site with a shovel. We built a system that turns a plain-language brief into a sampling campaign, flies it, and returns soil, water and vegetation samples with GPS, timestamps and chain of custody intact: 5,000 flights, roughly 9,000 miles and 570 flight hours over a 185-acre site in Roatan, much of it under canopy where GPS degrades and clearances are tight.

I will walk through the vision and planning stack, including multispectral and thermal canopy sensing, terrain and land-cover mapping, and the constraint solver that clears a route against airspace and battery budget before anything leaves the ground. Then the harder problem: evidentiary comparability.

A drone-collected result means nothing to a regulator unless it agrees with a hand-collected split under the same method, so I will show the dual-collection protocol we designed to test exactly that. I will close on why calibrated uncertainty and knowing when a model should refuse to answer matter more than raw accuracy once the output lands in a regulatory filing.

About the Speaker

Rohan Talwadia is co-founder and CTO of LabGPT, which automates environmental field sampling using drones and edge analysis, with results returned through ISO/IEC 17025-accredited partner laboratories.

Nov 09, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

Frontier multimodal models handle familiar concepts well and struggle on niche ones: sorting drill-core samples into 35 geological classes, or telling adhesion from scarring on a ceramic substrate. Our study across 11 specialized visual datasets in manufacturing inspection, geology, and medical imaging found that a few labeled examples in the prompt can close much of that gap, and it also found where that stops working.

What you’ll learn:

• Niche versus hard: how in-context examples supply domain knowledge a model is missing, and how to tell a niche task from a genuinely hard one
• What the study measured: on an industrial defect task, one labeled example per class raised macro-F1 from 34% to 59% with no fine-tuning, while on other tasks trained specialists pulled ahead at 5 to 10 examples per class
• Building a few-shot labeling task: define classes, pick reference examples, preview predictions on a small batch, and refine the instructions
• Finding the crossover point: when to stop prompting a foundation model and train a specialist on bootstrapped labels
• Running it on your own data: attendees get free GPU compute from Nebius to try the workflow after the session

Bring a laptop. Seats are limited. Pre-registration is mandatory.

Time, Date and Location

Nov 10, 2026
5:30-8:30 PM

We will be in Room SH-3620 of the Pavillon Sherbrooke at UQAM, 200 rue Sherbrooke Ouest, in the Quartier des Spectacles (Place-des-Arts metro).

The evening kicks off meetup-style with lightning talks from local speakers working across AI, machine learning, and computer vision, followed by a hands-on workshop on building physical AI applications with FiftyOne, from curating robotics and sensor datasets to evaluating vision models on real-world data.

A laptop is required to participate in the hands-on workshop - please bring one. Space is limited, so register early.

Check out the full agenda.

Note: A company, university, or government email address is required to take part in the hands-on workshop. Personal and disposable email accounts (Gmail, Yahoo, Outlook, and similar) cannot be used for the workshop environment. This is a blanket policy of the workshop platform to prevent fraud and abuse and to meet compliance requirements. If you need an exception, contact the hosts before the event.