FIBO-Sim2Real

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💡 Inspiration

Training robots and edge AI systems is one of the hardest and most expensive parts of deploying AI in the real world. For tasks like defect detection, object recognition, or quality inspection, teams need thousands of labeled images—often collected in unsafe, remote, or costly environments.

At the same time, most synthetic data tools lack precision, reproducibility, and real engineering control.

We were inspired to ask a simple question: What if one perfect image was enough to train a real-world AI system?

That question led us to build FIBO-Sim2Real Factory.

⚙️What it does

FIBO-Sim2Real Factory is an automated synthetic data factory that trains real computer-vision models using only one input image.

The system:

Takes a single Golden Image

Generates thousands of realistic, controllable variations using Bria FIBO’s JSON-native image generation

Automatically labels the images

Trains an object-detection model (YOLO)

Deploys the model to edge devices

Proves Sim2Real transfer by testing on real-world images

From one image → to a working edge AI model.

🛠️How we built it

We built the system end-to-end with a focus on engineering rigor and reproducibility.

Backend: Python + FastAPI

Synthetic data generation: Bria FIBO API with structured JSON controls

Dataset pipeline: Auto-generation of images, labels, and YOLO-ready datasets

Model training: YOLOv8 trained purely on synthetic data

Export: ONNX / TensorFlow Lite

Edge inference: Raspberry Pi / Jetson Nano demo

FIBO’s deterministic JSON control allowed us to mathematically vary camera, lighting, background, and materials—something traditional augmentation cannot do.

🚧Challenges we ran into

Designing synthetic variations that generalize well to real-world images

Balancing realism with diversity in generated data

Managing dataset size and training time during the hackathon

Proving Sim2Real transfer convincingly within limited time

Each challenge pushed us to rely more deeply on FIBO’s precise controls rather than random augmentation.

🏆Accomplishments that we’re proud of

Generated a full, labeled dataset from one single image

Trained a real object-detection model without using real training photos

Demonstrated Sim2Real transfer

Successfully exported and ran the model on an edge device

Built a system that cannot exist without FIBO

📚What we learned

Synthetic data is powerful only when it’s controllable and reproducible

Deterministic generation is critical for industrial AI

FIBO’s structured approach turns image generation into a true engineering tool

Sim2Real performance depends more on data diversity than data volume

🚀What’s next for FIBO-Sim2Real Factory

Add multi-object and multi-class generation

Support video-based synthetic datasets

Introduce domain-specific presets (manufacturing, agriculture, infrastructure)

Integrate active learning loops with real-world feedback

Scale the platform for robotics and industrial inspection teams

Built With

• fibo
• python
• yolo