YOLOv7 Tutorial

This tutorial will guide you through using trtutils with YOLOv7 models. We will cover:

1. Downloading ONNX weights from YOLOv7

2. Building a TensorRT engine

3. Running inference with the engine

4. Advanced features and optimizations

Downloading ONNX Weights

YOLOv7 models can be automatically downloaded and converted to ONNX format using the trtutils CLI:

# Download and convert YOLOv7 models to ONNX
# Available models: yolov7, yolov7x, yolov7-w6, yolov7-e6, yolov7-d6, yolov7-e6e
$ python3 -m trtutils download --model yolov7 --output yolov7.onnx --imgsz 640 --opset 17

# For other YOLOv7 variants
$ python3 -m trtutils download --model yolov7x --output yolov7x.onnx --imgsz 640 --opset 17

Building TensorRT Engine

Once you have the ONNX weights, you can build a TensorRT engine using trtutils:

# Note that build_yolo is not used since we exported the end2end model
# using the ONNX weights directly
python3 -m trtutils build \
    --onnx PATH_TO_WEIGHTS \
    --output PATH_TO_OUTPUT \
    --fp16

Alternatively, if you want to export the engine using the Python API:

from trtutils.builder import build_engine

# Build the engine with FP16 precision
build_engine(
    onnx="yolov7.onnx",
    output="yolov7.engine",
    fp16=True,
)

# For Jetson devices with DLA support
build_engine(
    onnx="yolov7.onnx",
    output="yolov7_dla.engine",
    int8=True,  # Orin series optimize for int8
    fp16=True,  # Can use fp16 on Xavier series
    dla_core=0,  # Use DLA core 0
)

Running Inference

The YOLO class provides a high-level interface for running YOLOv7 inference:

import cv2
from trtutils.models import YOLO, YOLOv7

# Load the YOLOv7 model
yolo = YOLO("yolov7.engine")

# OR, use the YOLOv7 class
yolo = YOLOv7("yolov7.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}")