yolotrt 1.0.0

YOLO to TensorRT auto-export pipeline and smart model loader. TRT > ONNX > PyTorch priority.

⚡ yolotrt

YOLO → TensorRT Auto-Export & Smart Model Loader

Export once, load the fastest format every time.

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✨ Why yolotrt?

Feature	yolotrt	Manual Export	torch2trt
One-line auto export	✅	❌	❌
Smart format priority (TRT > ONNX > PT)	✅	❌	❌
Works with Ultralytics YOLO	✅	✅	❌
FP16 by default	✅	Manual	✅
Track + Predict API pass-through	✅	❌	❌
Zero config needed	✅	❌	❌

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📦 Installation

pip install yolotrt

Note: Requires ultralytics and TensorRT runtime for GPU acceleration. pip install ultralytics — the TensorRT backend is optional.

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🚀 Quick Start

from yolotrt import ModelLoader

# First run: auto-exports .pt → .engine (5-10 min)
# Subsequent runs: loads .engine instantly
model = ModelLoader("models/best.pt", auto_trt=True, half=True)

# Inference (same API as ultralytics)
results = model.predict(frame, conf=0.5, verbose=False)
boxes = results[0].boxes.xyxy.cpu().numpy()

# Tracking
results = model.track(frame, persist=True, tracker="botsort.yaml")

Manual Export

from yolotrt import TRTExporter

exporter = TRTExporter("models/best.pt", half=True, imgsz=640)
if not exporter.is_exported():
    engine_path = exporter.export()
    print(f"Saved: {engine_path}")

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🔧 API Reference

ModelLoader(model_path, auto_trt=True, half=True, device=0, imgsz=640)

Smart loader that picks the fastest available format:

Priority	Format	Extension	Speed
1	TensorRT	.engine	⚡ Fastest
2	ONNX	.onnx	🔄 Cross-platform
3	PyTorch	.pt	🐢 Fallback

TRTExporter(model_path, half=True, device=0, imgsz=640)

Method	Returns	Description
export()	str	Run export, return engine path
is_exported()	bool	Check if .engine already exists

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📜 License

MIT License — use it anywhere.

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