🗣️ Introducing the Duality AI + LunateAI Challenge- Geospatial Object Detection: Rural Buildings! Train a model to detect difficult detection instances, such as a low number of pixels or weak feature responses, in rural aerial imagery, to win 🏆PRIZES🏆 and 🤩RECOGNITION🤩.
This is the first competition in the 🌎Geospatial Kaggle Challenge Series🌏, which will explore how geospatial-based digital twins can train an AI model for real-world applications.
Duality is excited to be partnering with LunateAI, a high-end advisory business founded by the award-winning, industry-recognized global leader Dr. Nadine Alameh to usher in a new era of geospatial impact in conjunction with advances in computing and AI. Lunate helps government and industry leaders 🤔 rethink, 💡redesign, and 📝 execute transformative geospatial strategies using AI, cloud, and Lunate’s unparalleled global expertise. Read more about the company here: https://lunateai.com/
🏠 For this Kaggle competition, competitors will train an object detection model to identify buildings in a rural environment, using aerial imagery. 🏠
The real-world testing data focuses on difficult detection instances, such as: 🔎 Low number of pixels 🏘️ Weak feature responses 🌤️Varied lighting
Competitors must generate varied synthetic data that addresses key difficulties of the real-world dataset using a FalconCloud simulation with controllable parameters.
Competitors can control: ✅ Variety of in-class of objects ✅ Lighting and shadow conditions ✅ Environmental occlusions ✅ and more!
Join the competition and learn how digital simulation using data such as geospatial information can help tackle difficult real-world challenges.
Duality AI's latest Kaggle competition was our most difficult one yet! Contestants had to train for Multiclass object detection using data from FalconCloud (you can still use the scenarios for free here - https://tinyurl.com/5n7yhvha , https://tinyurl.com/38ktjfkn)
Duality partnered with @3LC and their software for creating better, smaller, faster AI models!
The following winners showed skill and perseverance, analyzing and generating targeted data that trains a model to works on real-world data 👏 👏 👏
🏎️ Generative AI promises to fast-track deployable Physical AI — but turning that promise into reliable training data for robots, autonomous systems, and even embodied AI isn’t automatic.
⛓️💥 The missing link? Bridging the 𝐆𝐞𝐧2𝐑𝐞𝐚𝐥 𝐆𝐚𝐩 — the distance between what generative models produce and how the real world behaves.
In our latest blog, Duality AI CEO Apurva Shah shares how lessons from closing the Sim2Real gap point the way forward for Gen2Real, including:
💡 Understanding the fundamentals of Implicit vs. Explicit world models. 🏗️ Designing hybrid synthetic data pipelines and agentic workflows that combine the strengths of simulated and generative approaches while minimizing their weaknesses. 📊 Applying rigorous metrics so synthetic data doesn’t just look real, but accurately predicts reality in areas vital for Physical AI training.
🎉 Big congratulations to the winners of the "Synthetic 2 Real Object Detection Challenge 2", the second Kaggle challenge that Duality AI hosted. This competition was more fierce than the last one, but these users managed to clench the win!
Shout out to the winners of the "Synthetic2Real Object Detection Challenge" Duality AI hosted earlier this year. Out of the 1000+ participants in our challenges, these users stood out above the rest.
It covers key features of targeted data for successful synthetic data creation and model training.
This is part 1, is this useful to y'all? Would you like more articles like this or on other topics from our experts?
As always, you can start creating your own synthetic data for free on Falcon. It's not Gen AI, its data crafted from digital scenarios, designed to align with a target domain.
When you're looking for data, what's your focus (use the reactions below to vote): 🚀 Getting as many images as you can 🤯 Getting the right type of images (framing, domain, lighting, etc)
I know both are very important, but I'm curious what people would put as #1
NEW ARTICLE: "Detecting Beyond Sight: Building AI-Enabled SAR Intelligence with Synthetic Data"
Synthetic Aperture Radar (SAR) reveals what optical sensors can’t. AI can turn that information into actionable intelligence—but only with the right training data.
In our latest blog, we explore how Falcon’s new virtual SAR sensor solves the SAR data bottleneck for AI development. As the newest addition to Falcon’s sensor library, it models radar returns with precision—including azimuth, range resolution, signal intensity, and noise. This Falcon-specific, GPU-accelerated raytraced SAR model is exposed via Falcon’s Python API, giving teams precise, control over radar wave propagation and enabling physically grounded, highly customizable, and user-friendly SAR simulation.
The result? High-fidelity, automatically labeled synthetic SAR imagery from any scenario—on demand. No custom setup. No external workflows. Just mission-ready data for building AI models across defense, disaster response, agriculture, intelligence, and beyond.
Excuse the lag, it's from the real-time inference from the webcam 👀 . Did you know that YOLOv11 added Streamlit for live object detection straight from your webcam?
📢 Generate your own data in simulation using two new free and customizable data-generating Scenarios on Duality's FalconCloud service. 🙌 These multi-class Scenarios are designed to target model weaknesses for our recent Kaggle competition, but they are free to anyone for non-commercial use! Just create a free account.
📸 Control object and camera posing 👉 Select random variable ranges 🖼️ Set post-processing effects ➕ and more to create a robust dataset for strong model training.
🤝 Duality AI is partnering in this competition with 3LC.AI, a cutting-edge platform which enables users to capture per sample metrics and take meaningful action leading to better, faster, and smaller AI models.
Competitors will be challenged to: ✨ Create customized training data with Duality’s cloud-based scenario ✨Analyze data weaknesses and make calculated changes using 3LC’s robust software ✨Optimize data for peak model training
Compete for prizes, certificates, and recognition from peer competitors around the world. Whether you’re a student, researcher, or industry pro, this challenge offers hands-on experience customizing high-fidelity synthetic data for robust models.
Ready to bridge the Sim2Real gap? Join us and start building today!
🤔 Ready to build better AI models with synthetic data, but don't know where to start? Why go at it alone?💡
👋 Join Duality AI’s Falcon community! It is one of the best resources for support, creativity, and growth as you move along your synthetic data journey.
🌟Mohana pavan Bezawada, @mohanapavan, who has risen in the ranks from the top 25 in the first competition all the way to top scorer in our current competition! His journey illustrates how dedication + Falcon can take you far in your AI journey.
🌟Nadia TRIKI, who delivered top-tier results in two of our recent Kaggle competitions and shared a detailed breakdown of her strategy - showcasing a deep command of AI training workflows and a commitment to helping others succeed.
Ángel Jacinto Sánchez Ruiz, @Sacus , who mastered FalconCloud to create targeted, high-performance datasets and provided crucial feedback and product requests that improved the data not only for him but for all of the current competitors.
🤩 Join our community today to partner with these super stars, and many more!
Can AI models trained solely on 100% synthetic data achieve top-tier accuracy in real-world object detection?
👉 @sergio-sanz-rodriguez just proved it while winning Duality AI’s Synthetic-to-Real Object Detection Challenge using Falcon-generated imagery. His model achieved perfect real-world detection accuracy without a single real image in the training loop.
In this blog, Dr. Sanz walks us through his method, which includes the design and training of an advanced pipeline to achieve 100% detection accuracy. His full technical breakdown covers: 📍 Synthetic-only training 📍 Data augmentation with an ensemble learning approach for better generalization 📍 Custom occlusion generation 📍 A Faster R-CNN model fine-tuned with Falcon generated data 📍 And much more!
Significant threats to AI model performance aren’t always loud or obvious. Integrity violations—like subtle data poisoning attacks—can quietly erode your model’s reliability, long before anyone notices. These attacks can be surprisingly effective with minimal changes to the dataset.
At Duality, our work in high-stakes sectors like defense has driven us to tackle this threat head-on. In our latest blog from Duality's Director of Infrastructure and Security at Duality, David Strout, we unpack how data poisoning works, why it’s so dangerous, and how organizations can secure their AI pipelines with clear provenance, regular performance auditing, and a trusted synthetic data supply chain.
Whether you're building AI models for finance, healthcare, manufacturing, or national security—the integrity of these systems is a matter of public safety and security. Taking action today will mitigate fundamental business risks in the very near tomorrow.
As part of Duality AI’s recent Kaggle competition, we’ve released a free, fully customizable cloud scenario designed to help you create targeted datasets with YOLO-compatible labels.
The cloud simulation lets you customize the: 📸 camera distance 🎞️ film grain variation 🖼️background objects, ➕ and more!
I’ve attached an instructional video we used for the competition, but this feature is free for anyone who has an account. https://vimeo.com/1091271731?share=copy
This competition will test users' ability to train a model for multi-instance object detection. Users will: ✨Customize a cloud-based simulation ✨Output unique data for robust model training ✨Optimize training for peak model performance
Compete for cash prizes, certificates, and recognition from peer competitors around the world. Whether you’re a student, researcher, or industry pro, this challenge offers hands-on experience customizing high-fidelity synthetic data for robust models. Ready to bridge the Sim2Real gap? Join us and start building today!
After the overwhelming response to Challenge 1, we're pushing the boundaries even further in Challenge 2, where your object detection models will be put to the test in the real world after training only on synthetic data.
👉 Join our Synthetic-to-Real Object Detection Challenge 2 on Kaggle!
What’s Different This Time? Unlike our first challenge, we’re now diving deep into data manipulation. Competitors can:
🔹Access 4 new supplemental datasets via FalconCloud with varying lighting, occlusions, and camera angles. 🔹Generate your own synthetic datasets using FalconEditor to simulate edge cases. 🔹Mix, match, and build custom training pipelines for maximum mAP@50 performance
This challenge isn’t just about using synthetic data—it’s about mastering how to craft the right synthetic data. Ready to test your skills?
🏆The Challenge Train an object detection model using synthetic images created with Falcon—Duality AI's cutting-edge digital twin simulation software—then evaluate your model on real-world imagery.
The Twist?
📈Boost your model’s accuracy by creating and refining your own custom synthetic datasets using Falcon!
Win Cash Prizes & Recognition 🔹Earn cash and public shout-outs from the Duality AI accounts Enhance Your Portfolio 🔹Demonstrate your real-world AI and ML expertise in object detection to prospective employers and collaborators. 🔹Expand Your Network 🔹Engage, compete, and collaborate with fellow ML engineers, researchers, and students. 🚀 Put your skills to the test and join our Kaggle competition today: https://lnkd.in/g2avFP_X
