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People (4 results)
See all 4 →Activities & events
| Title & Speakers | Event |
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Best Datadog Configuration at Scale – A Volkswagen Case Study
2025-10-29 · 18:40
Santiago Gómez Sáez
– Principal Cloud Architect
@ dx.one (Volkswagen Group)
Best Datadog configuration at scale—Volkswagen Group case study. |
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Scaling LLM Evaluation: A Cloud-Native Approach with Phoenix
2025-10-29 · 18:40
Cloud-native approach to scaling LLM evaluation. |
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Automating Agent Observability and Evals: Real Lessons from the Industry
2025-10-29 · 18:40
Dat Daryl Ngo
– Director of AI Solutions
@ Arize AI (EMEA/APJ)
Real lessons from the industry on automating agent observability and evaluations. |
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June 18 - Getting Started with FiftyOne Workshop
2025-06-18 · 16:00
When and Where June 18\, 2025 \| 9:00 – 10:30 AM Pacific About the Workshop Want greater visibility into the quality of your computer vision datasets and models? Then join us for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. At the end of the workshop you’ll be able to:
This workshop will explore the importance of taking a data-centric approach to computer vision workflows. We will start with importing and exploring visual data, then move to querying and filtering. Next, we’ll look at ways to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. We’ll generate candidate ground truth labels, and then wrap things up by evaluating the results of fine tuning a foundational model. Prerequisites: working knowledge of Python and basic computer vision concepts. All attendees will get access to the tutorials, videos, and code examples used in the workshop 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. |
June 18 - Getting Started with FiftyOne Workshop
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Getting started with FiftyOne (90 min workshop)
2025-06-18 · 16:00
Join this free, 90-minute hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset for greater visibility into the quality of your computer vision datasets and models. The workshop will cover:
The session emphasizes a data-centric approach to computer vision workflows. Participants will start by importing and exploring visual data, then move on to querying and filtering. The workshop will also demonstrate how to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. Attendees will learn to generate candidate ground truth labels and evaluate the results of fine-tuning a foundational model. Prerequisites: Working knowledge of Python and basic computer vision concepts. All attendees will receive access to the tutorials, videos, and code examples used in the workshop Instructor: Antonio Rueda-Toicen, AI Engineer in Berlin and Research Scientist at the Hasso Plattner Institute, will lead the session. Antonio has extensive experience deploying machine learning models and has taught over 300 professionals. He specializes in computer vision, cloud technologies, and machine learning, and is a certified instructor of deep learning and diffusion models in NVIDIA’s Deep Learning Institute. |
Getting started with FiftyOne (90 min workshop)
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June 18 - Getting Started with FiftyOne Workshop
2025-06-18 · 16:00
When and Where June 18\, 2025 \| 9:00 – 10:30 AM Pacific About the Workshop Want greater visibility into the quality of your computer vision datasets and models? Then join us for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. At the end of the workshop you’ll be able to:
This workshop will explore the importance of taking a data-centric approach to computer vision workflows. We will start with importing and exploring visual data, then move to querying and filtering. Next, we’ll look at ways to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. We’ll generate candidate ground truth labels, and then wrap things up by evaluating the results of fine tuning a foundational model. Prerequisites: working knowledge of Python and basic computer vision concepts. All attendees will get access to the tutorials, videos, and code examples used in the workshop 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. |
June 18 - Getting Started with FiftyOne Workshop
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June 18 - Getting Started with FiftyOne Workshop
2025-06-18 · 16:00
When and Where June 18\, 2025 \| 9:00 – 10:30 AM Pacific About the Workshop Want greater visibility into the quality of your computer vision datasets and models? Then join us for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. At the end of the workshop you’ll be able to:
This workshop will explore the importance of taking a data-centric approach to computer vision workflows. We will start with importing and exploring visual data, then move to querying and filtering. Next, we’ll look at ways to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. We’ll generate candidate ground truth labels, and then wrap things up by evaluating the results of fine tuning a foundational model. Prerequisites: working knowledge of Python and basic computer vision concepts. All attendees will get access to the tutorials, videos, and code examples used in the workshop 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. |
June 18 - Getting Started with FiftyOne Workshop
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June 18 - Getting Started with FiftyOne Workshop
2025-06-18 · 16:00
When and Where June 18\, 2025 \| 9:00 – 10:30 AM Pacific About the Workshop Want greater visibility into the quality of your computer vision datasets and models? Then join us for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. At the end of the workshop you’ll be able to:
This workshop will explore the importance of taking a data-centric approach to computer vision workflows. We will start with importing and exploring visual data, then move to querying and filtering. Next, we’ll look at ways to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. We’ll generate candidate ground truth labels, and then wrap things up by evaluating the results of fine tuning a foundational model. Prerequisites: working knowledge of Python and basic computer vision concepts. All attendees will get access to the tutorials, videos, and code examples used in the workshop 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. |
June 18 - Getting Started with FiftyOne Workshop
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June 18 - Getting Started with FiftyOne Workshop
2025-06-18 · 16:00
When and Where June 18\, 2025 \| 9:00 – 10:30 AM Pacific About the Workshop Want greater visibility into the quality of your computer vision datasets and models? Then join us for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. At the end of the workshop you’ll be able to:
This workshop will explore the importance of taking a data-centric approach to computer vision workflows. We will start with importing and exploring visual data, then move to querying and filtering. Next, we’ll look at ways to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. We’ll generate candidate ground truth labels, and then wrap things up by evaluating the results of fine tuning a foundational model. Prerequisites: working knowledge of Python and basic computer vision concepts. All attendees will get access to the tutorials, videos, and code examples used in the workshop 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. |
June 18 - Getting Started with FiftyOne Workshop
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June 18 - Getting Started with FiftyOne Workshop
2025-06-18 · 16:00
When and Where June 18\, 2025 \| 9:00 – 10:30 AM Pacific About the Workshop Want greater visibility into the quality of your computer vision datasets and models? Then join us for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. At the end of the workshop you’ll be able to:
This workshop will explore the importance of taking a data-centric approach to computer vision workflows. We will start with importing and exploring visual data, then move to querying and filtering. Next, we’ll look at ways to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. We’ll generate candidate ground truth labels, and then wrap things up by evaluating the results of fine tuning a foundational model. Prerequisites: working knowledge of Python and basic computer vision concepts. All attendees will get access to the tutorials, videos, and code examples used in the workshop 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. |
June 18 - Getting Started with FiftyOne Workshop
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June 18 - Getting Started with FiftyOne Workshop
2025-06-18 · 16:00
When and Where June 18\, 2025 \| 9:00 – 10:30 AM Pacific About the Workshop Want greater visibility into the quality of your computer vision datasets and models? Then join us for this free 90-minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. At the end of the workshop you’ll be able to:
This workshop will explore the importance of taking a data-centric approach to computer vision workflows. We will start with importing and exploring visual data, then move to querying and filtering. Next, we’ll look at ways to extend FiftyOne’s functionality and simplify tasks using plugins and native integrations. We’ll generate candidate ground truth labels, and then wrap things up by evaluating the results of fine tuning a foundational model. Prerequisites: working knowledge of Python and basic computer vision concepts. All attendees will get access to the tutorials, videos, and code examples used in the workshop 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. |
June 18 - Getting Started with FiftyOne Workshop
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May 22 - AI, ML and Computer Vision Meetup
2025-05-22 · 17:00
When and Where
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. |
May 22 - AI, ML and Computer Vision Meetup
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May 22 - AI, ML and Computer Vision Meetup
2025-05-22 · 17:00
When and Where
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. |
May 22 - AI, ML and Computer Vision Meetup
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May 22 - AI, ML and Computer Vision Meetup
2025-05-22 · 17:00
When and Where
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. |
May 22 - AI, ML and Computer Vision Meetup
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May 22 - AI, ML and Computer Vision Meetup
2025-05-22 · 17:00
When and Where
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. |
May 22 - AI, ML and Computer Vision Meetup
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May 22 - AI, ML and Computer Vision Meetup
2025-05-22 · 17:00
When and Where
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. |
May 22 - AI, ML and Computer Vision Meetup
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May 22 - AI, ML and Computer Vision Meetup
2025-05-22 · 17:00
When and Where
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. |
May 22 - AI, ML and Computer Vision Meetup
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May 22 - AI, ML and Computer Vision Meetup
2025-05-22 · 17:00
When and Where
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. |
May 22 - AI, ML and Computer Vision Meetup
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May 20 - Image Generation: Diffusion Models & U-Net Workshop
2025-05-20 · 16:30
When and Where
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. |
May 20 - Image Generation: Diffusion Models & U-Net Workshop
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Practical Computer Vision with PyTorch and FiftyOne
2025-05-20 · 16:30
About the Workshop Series 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. 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 These hands-on workshops make use of GitHub Codespaces and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!). You can find the workshop materials in this GitHub repository: https://github.com/andandandand/practical-computer-vision About the Instructor Antonio Rueda-Toicen, 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 and an AI Engineer and DevRel for Voxel51. 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 at NVIDIA’s Deep Learning Institute. Workshop 1 – Foundations of Computer Vision Tuesday, March 4, 2025 In this session, we’ll introduce core computer vision tasks and fundamental image representations using PIL, NumPy, and PyTorch. Build a simple neural network to classify handwritten digits and explore dataset visualizations with FiftyOne. Workshop 2 – Neural Networks Fundamentals: Multilayer Perceptrons for Regression Tuesday, March 11 In this session, we’ll cover the basics of neural networks, build a multilayer perceptron (MLP) and delve into matrix multiplications. Participants will create an MLP for regression (predicting car prices) and inspect its forward pass and predictions with FiftyOne. Workshop 3 – Training & Evaluation of Classification Models Tuesday, March 18 In this session, we’ll focus on training feedforward networks and evaluating model performance. Learn about datasets, data loaders, and classification metrics, then apply these concepts by classifying breeds of dogs and analyzing outputs with FiftyOne. Workshop 4 – Convolutional Neural Networks - LeNet5 Tuesday, March 25 In this session, we’ll explore CNN fundamentals by diving into the mechanics of convolutions and pooling. Participants will implement LeNet5 to grasp how basic convolutional layers operate, with practical insights on the features produced by convolutions using FiftyOne. Workshop 5 – Training Techniques for Convolutional Networks Tuesday, April 1 In this session, we’ll examine strategies such as normalization and skip connections for better training. Build a model to run inference on a fruits dataset, then use FiftyOne to inspect predictions and performance. Workshop 6 – Multi-label Classification with Binary Cross Entropy: Amazon Satellite Images Tuesday, April 8 In this session, we’ll focus on multi-label classification for real-world imagery. Build a model that identifies multiple environmental labels from Amazon satellite images, applying binary cross-entropy for training and analyzing predictions with FiftyOne. Workshop 7 – Interpretability in Computer Vision: CAM & Grad-CAM Tuesday, April 15 In this session, we’ll learn interpretability techniques such as Class Activation Mapping and Grad-CAM. Build a model to analyze predictions and visualize important image regions with FiftyOne. Workshop 8 – Convolutional Neural Networks: Advanced Upsampling & U-Net for Semantic Segmentation Tuesday, April 22 In this session, we’ll delve deeper into CNN architectures by focusing on upsampling, channel mixing, and semantic segmentation techniques. Build a U-Net model for semantic image segmentation and inspect its predictions with FiftyOne. Workshop 9 – Model Optimization: Data Augmentation & Regularization Tuesday, April 29 In this session, we’ll introduce optimization strategies including data augmentation, dropout, batch normalization, and transfer learning. Implement an augmented network using a fruits dataset with models like VGG-16 and ResNet18, and analyze the results with FiftyOne. Workshop 10 – Image Embeddings: Zero-shot Classification with CLIP Tuesday, May 6 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. Workshop 11 – Object Detection & Instance Segmentation: YOLO in Practice Tuesday, May 13 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. Workshop 12 – Image Generation: Diffusion Models & U-Net Tuesday, May 20 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. |
Practical Computer Vision with PyTorch and FiftyOne
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