What we’re about
MLconf is a single day, single track conference, focusing on the current application of machine learning algorithms, techniques, tools & methods.
Upcoming events (4+)
See all- Network event105 attendees from 36 groups hostingMay 6 - Image Embeddings: Zero-shot Classification with CLIP WorkshopLink visible for attendees
When and Where
- May 6, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, we’ll cover image embeddings, vision transformers, and CLIP. Build a model for zero-shot classification and semantic search using CLIP, then inspect how image embeddings influence predictions with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlin’s Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute.
- Network event73 attendees from 36 groups hostingMay 13 - Object Detection & Instance Segmentation: YOLO in Practice WorkshopLink visible for attendees
When and Where
- May 13, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, we’ll introduce object detection and instance segmentation methods. Build a YOLO-based network to perform object detection and instance segmentation, and analyze detection results with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlin’s Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute.
- Network event41 attendees from 36 groups hostingMay 20 - Image Generation: Diffusion Models & U-Net WorkshopLink visible for attendees
When and Where
- May 20, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, we’ll explore image generation techniques using diffusion models. Participants will build a U-Net-based model to generate MNIST-like images and then inspect the generated outputs with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlin’s Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute.
- Network event129 attendees from 36 groups hostingMay 22 - AI, ML and Computer Vision MeetupLink visible for attendees
When and Where
- May 22, 2025 | 10:00 AM Pacific
- Virtual - Register for the Zoom
CountGD: Multi-Modal Open-World Counting
We propose CountGD, the first open-world counting model that can count any object specified by text only, visual examples only, or both together. CountGD extends the Grounding DINO architecture and adds components to enable specifying the object with visual examples. This new capability – being able to specify the target object by multi-modalites (text and exemplars) – lead to an improvement in counting accuracy. CountGD is powering multiple products and has been applied to problems across different domains including counting large populations of penguins to monitor the influence of climate change, counting buildings from satellite images, and counting seals for conservation.
About the Speaker
Niki Amini-Naieni is a DPhil student focusing on developing foundation model capabilities for visual understanding of the open world at the Visual Geometry Group (VGG), Oxford supervised by Andrew Zisserman. In the past, Niki has consulted with Amazon and other companies in robotics and computer vision, interned at SpaceX, and studied computer science and engineering at Cornell.
GorillaWatch: Advancing Gorilla Re-Identification and Population Monitoring with AI
Accurate monitoring of endangered gorilla populations is critical for conservation efforts in the field, where scientists currently rely on labor-intensive manual video labeling methods. The GorillaWatch project applies visual AI to provide robust re-identification of individual gorillas and generate local population estimates in wildlife encounters.
About the Speaker
Maximilian von Klinski is a Computer Science student at the Hasso-Plattner-Institut and is currently working on the GorillaWatch project alongside seven fellow students.
This Gets Under Your Skin – The Art of Skin Type Classification
Skin analysis is deceptively hard: inconsistent portrait quality, lighting variations, and the presence of sunscreen or makeup often obscure what’s truly “under the skin.” In this talk, I’ll share how we built an AI pipeline for skin type classification that tackles these real-world challenges with a combination of vision models. The architecture includes image quality control, facial segmentation, and a final classifier trained on curated dermatological features.
About the Speaker
Markus Hinsche is the co-founder and CTO of Thea Care, where he builds AI-powered skincare solutions at the intersection of health, beauty, and longevity. He holds a Master’s in Software Engineering from the Hasso Plattner Institute and brings a deep background in AI and product development.
A Spot Pattern Is like a Fingerprint: Jaguar Identification Project
The Jaguar Identification Project is a citizen science initiative actively 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 use this rich dataset for dual purposes: teaching data-centric visual AI and directly contributing to conservation efforts for this vulnerable species.
About the Speaker
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlin’s Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute.
Past events (50)
See all- Network event201 attendees from 36 groups hostingApril 29 - Model Optimization: Data Augmentation & Regularization WorkshopThis event has passed