# 🧠 TTA-S2R: Pretrained YOLO Models Collection

This repository contains the pretrained models used in the **TTA-S2R (Tidal Turbine Assembly – Sim2Real)** pipeline.  
All models were trained and evaluated as part of the study on **sim-to-real transfer for industrial assembly object detection**.

The collection includes YOLOv8, YOLOv9, and YOLO-World models trained under three regimes:
- **Controlled only** – trained purely on real controlled data captured with a collaborative robot.
- **Synthetic only** – trained purely on domain-randomized synthetic data.
- **Synthetic + Controlled** – fine-tuned using both sources for improved sim-to-real generalization.

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## 🧩 Usage

You can load any model directly in Python using the [Ultralytics](https://github.com/ultralytics/ultralytics) framework:

```python
from ultralytics import YOLO, YOLOWorld

# Example for YOLOv8 or YOLOv9
model = YOLO("best_model_8_synthetic_controlled.pt")

# Example for YOLO-World
model = YOLOWorld("best_model_yoloworld_synthetic_controlled.pt")

results = model.predict(source="path/to/image_or_video.jpg")