April 24 - Berlin AI, ML and Computer Vision Meetup

About this event

Join our in-person meetup on April 24th to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register to reserve your seat. Space is limited!

Date, Time and Location

Apr 24, 2026
5:30 PM - 8:30 PM

MotionLab
Bouchéstraße 12/Halle 20
12435 Berlin

Kaputt: A Large-Scale Dataset for Visual Defect Detection

We present a novel large-scale dataset for defect detection in a logistics setting. Recent work on industrial anomaly detection has primarily focused on manufacturing scenarios with highly controlled poses and a limited number of object categories. Existing benchmarks like MVTec-AD (Bergmann et al., 2021) and VisA (Zou et al., 2022) have reached saturation, with state-of-the-art methods achieving up to 99.9% AUROC scores. In contrast to manufacturing, anomaly detection in retail logistics faces new challenges, particularly in the diversity and variability of object pose and appearance. Leading anomaly detection methods fall short when applied to this new setting.

To bridge this gap, we introduce a new benchmark that overcomes the current limitations of existing datasets. With over 230,000 images (and more than 29,000 defective instances), it is 40 times larger than MVTec and contains more than 48,000 distinct objects. To validate the difficulty of the problem, we conduct an extensive evaluation of multiple state-of-the-art anomaly detection methods, demonstrating that they do not surpass 56.96% AUROC on our dataset. Further qualitative analysis confirms that existing methods struggle to leverage normal samples under heavy pose and appearance variation. With our large-scale dataset, we set a new benchmark and encourage future research towards solving this challenging problem in retail logistics anomaly detection. The dataset is available for download under https://www.kaputt-dataset.com.

About the Speaker

Sebastian Höfer is an Applied Science Manager at Amazon Fulfillment Technologies & Robotics, leading machine learning and computer vision research for large-scale robotics and warehouse automation. He received his PhD from the Robotics & Biology Lab at TU Berlin, focusing on Sim2Real transfer and robotic perception. His recent work, “Kaputt: A Large-Scale Dataset for Visual Defect Detection” (ICCV 2025) [37], established a major benchmark for industrial anomaly detection, reflecting his expertise at the intersection of academic research and real-world deployment.

Data Foundations for Vision-Language-Action Models

Model architectures get the papers, but data decides whether robots actually work. This talk introduces VLAs from a data-centric perspective: what makes robot datasets fundamentally different from image classification or video understanding, how the field is organizing its data (Open X-Embodiment, LeRobot, RLDS), and what evaluation benchmarks actually measure. We'll examine the unique challenges such as temporal structure, proprioceptive signals, and heterogeneity in embodiment, and discuss why addressing them matters more than the next architectural innovation.

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 VLMs, Visual Agents, Document AI, and Physical AI.

Most AI Agents Are Broken. Let’s Fix That

AI agents are having a moment, but most of them are little more than fragile prototypes that break under pressure. Together, we’ll explore why so many agentic systems fail in practice, and how to fix that with real engineering principles. In this talk, you’ll learn how to build agents that are modular, observable, and ready for production. If you’re tired of shiny agent demos that don't deliver, this talk is your blueprint for building agents that actually work.

About the Speaker

Bilge Yücel is a Senior Developer Relations Engineer at deepset, helping developers build agentic AI apps with Haystack. Passionate about AI, she makes complex concepts approachable through hands-on tutorials, both online and at real-life events.

A Spot Pattern is Like a Fingerprint: Jaguar Identification Kaggle Challenge

The Jaguar Identification Project is a citizen science initiative engaging the public in conservation efforts in Porto Jofre, Brazil. This project increases awareness and provides an interesting and challenging dataset that requires the use of fine-grained visual classification algorithms. We discuss its ongoing Kaggle Challenge and winning strategies for it based on dataset curation and representation learning. -https://www.kaggle.com/competitions/jaguar-re-id

About the Speaker

Antonio Rueda-Toicen is a machine learning consultant at Kineto.ai and a Researcher at the Artificial Intelligence and Intelligent Systems Chair at the Hasso Plattner Institute. He organizes the Berlin Computer Vision Group and is a certified instructor at NVIDIA's Deep Learning Institute.