YOLOv9 Tutorial

This tutorial will guide you through using trtutils with YOLOv9 models. We will cover:

1. Downloading ONNX weights from YOLOv9

2. Building a TensorRT engine

3. Running inference with the engine

Downloading ONNX Weights

YOLOv9 models can be automatically downloaded and converted to ONNX format using the trtutils CLI:

# Download and convert YOLOv9 models to ONNX
# Available models: yolov9, yolov9-c, yolov9-e, yolov9-m, yolov9-s, yolov9-t
$ python3 -m trtutils download --model yolov9 --output yolov9.onnx --imgsz 640 --opset 17

# For other YOLOv9 variants
$ python3 -m trtutils download --model yolov9-c --output yolov9-c.onnx --imgsz 640 --opset 17

Building TensorRT Engine

When building the TensorRT engine for YOLOv9, you need to explicitly specify the input shape since it’s dynamic in the ONNX weights:

from trtutils.builder import build_engine

# Build the engine with FP16 precision
# Note: The input shape must match the img-size used during export
build_engine(
    onnx="yolov9.onnx",
    output="yolov9.engine",
    fp16=True,
    shapes=[("images", (1, 3, 640, 640))],  # Must match export img-size
)

# For Jetson devices with DLA support
build_engine(
    onnx="yolov9.onnx",
    output="yolov9_dla.engine",
    fp16=True,
    shapes=[("images", (1, 3, 640, 640))],
    dla_core=0,  # Use DLA core 0
)

Running Inference

The YOLO class provides a high-level interface for running YOLOv9 inference:

import cv2
from trtutils.models import YOLO, YOLOv9

# Load the YOLOv9 model
yolo = YOLO("yolov9.engine")

# OR, use the YOLOv9 class
yolo = YOLOv9("yolov9.engine")

# Read and process an image
img = cv2.imread("example.jpg")
detections = yolo.end2end(img)

# Print results
for bbox, confidence, class_id in detections:
    print(f"Class: {class_id}, Confidence: {confidence}")
    print(f"Bounding Box: {bbox}")