🚀 Announcing the Synthetic-to-Real Multi-Class Object Detection Challenge!
We’re excited to announce the launch of the Synthetic-to-Real Multi-Class Object Detection Challenge—now live on Kaggle!
This exciting competition is brought to you by 3LC in partnership with Duality AI, creators of the powerful FalconCloud tool for generating targeted synthetic data. Together, we're offering a unique opportunity to push the boundaries of object detection through high-fidelity, simulation-to-real workflows.
🧪 What Makes This Challenge Special? 💻 Create customized training data with Duality’s cloud-based scenario 🧠 Analyze data weaknesses and take precise, data-driven actions using 3LC's robust tooling ⚙️ Optimize data for peak model training
🏆 Why Join? • Win cash prizes, certificates, and global recognition • Gain exposure to real-world simulation workflows used in top AI companies • Collaborate and compete with leading minds in computer vision, ML, and AI
Whether you're a student, researcher, or industry pro, this challenge is your chance to bridge the Sim2Real gap and showcase your skills in building high-performance object detection models.
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.
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!
