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16-Model Prediction on Jetson

This section explains how to perform model prediction (inference) on NVIDIA Jetson using trained YOLO models in PyTorch, ONNX, or TensorRT formats.

1. Best Performance Mode​

Before running inference, configure Jetson for maximum performance.

1.1 Enable MAX Power Mode​

sudo nvpmodel -m 2

1.2 Enable Jetson Clocks​

sudo jetson_clocks

2. Model Prediction​

2.1 CLI Usage​

note

The YOLO CLI currently supports USB cameras only. For CSI cameras, use Python-based inference.

Run prediction with a TensorRT engine model:

yolo predict model=best.engine source=0 save=False show
  • source=0: USB camera index\
  • For multiple cameras, increment the index accordingly

Output videos are saved to:

/home/jetson/ultralytics/ultralytics/output/

CLI Prediction Result

2.2 Python Usage​

Python-based inference supports both USB cameras and CSI cameras.

2.2.1 USB Camera Prediction​

cd /home/jetson/ultralytics/ultralytics/yahboom_demo

Run USB Camera Script​

python3 06.orange_camera_usb.py

Press q to exit the preview window.

Output Preview​

/home/jetson/ultralytics/ultralytics/output/

USB Camera Result

Sample Code (USB Camera)​

import cv2
from ultralytics import YOLO

model = YOLO(
    "/home/jetson/ultralytics/ultralytics/data/yahboom_data/orange_data/"
    "runs/detect/train/weights/best.engine"
)

cap = cv2.VideoCapture(0)

frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))

out = cv2.VideoWriter(
    "/home/jetson/ultralytics/ultralytics/output/orange_usb.mp4",
    cv2.VideoWriter_fourcc(*"mp4v"),
    fps,
    (frame_width, frame_height),
)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break

    results = model(frame)
    annotated = results[0].plot()
    out.write(annotated)

    cv2.imshow("YOLO Inference", cv2.resize(annotated, (640, 480)))
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
out.release()
cv2.destroyAllWindows()

2.2.2 CSI Camera Prediction​

cd /home/jetson/ultralytics/ultralytics/yahboom_demo

Run CSI Camera Script​

python3 06.orange_camera_csi.py

Press q to exit the preview window.

Sample Code (CSI Camera)​

import cv2
from ultralytics import YOLO
from jetcam.csi_camera import CSICamera

model = YOLO(
    "/home/jetson/ultralytics/ultralytics/data/yahboom_data/orange_data/"
    "runs/detect/train/weights/best.engine"
)

camera = CSICamera(width=640, height=480)

while True:
    frame = camera.read()
    results = model(frame)
    annotated = results[0].plot()

    cv2.imshow("YOLO CSI Inference", annotated)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cv2.destroyAllWindows()

CSI Camera Result

Summary​

  • Supports inference using .pt, .onnx, and .engine models
  • CLI inference is limited to USB cameras
  • Python inference supports USB and CSI cameras
  • TensorRT (.engine) provides the best performance

Maintained by HemiHex for Jetson-based advanced vision workflows.