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Image Classification on Jetson

This section demonstrates image classification on NVIDIA Jetson using Ultralytics YOLO classification models. Examples include image, video, and real-time camera classification.

1. Optimize Jetson Performance​

Before running classification, ensure Jetson is operating at maximum performance.

Enable MAX Power Mode​

sudo nvpmodel -m 2

Enable Jetson Clocks​

sudo jetson_clocks

2. Image Classification (Image Input)​

Enter Demo Directory​

cd ~/ultralytics/ultralytics/yahboom_demo

Run Image Classification Script​

python3 04.classification_image.py

Results are saved to:

~/ultralytics/ultralytics/output/

Sample Code (Image Classification)​

from ultralytics import YOLO

model = YOLO("yolo11n-cls.pt")
results = model("assets/dog.jpg")

for r in results:
    r.show()
    r.save(filename="output/dog_output.jpg")

3. Image Classification (Video Input)​

Run Video Classification Script​

python3 04.classification_video.py

Output video location:

~/ultralytics/ultralytics/output/

Sample Code (Video Classification)​

import cv2
from ultralytics import YOLO

model = YOLO("yolo11n-cls.pt")
cap = cv2.VideoCapture("videos/cup.mp4")

width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))

out = cv2.VideoWriter(
    "output/cup_output.mp4",
    cv2.VideoWriter_fourcc(*"mp4v"),
    fps,
    (width, height)
)

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

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

cap.release()
out.release()

4. Real-Time Image Classification​

  • USB Camera: python3 04.classification_usb_cam.py
  • CSI Camera: python3 04.classification_csi_cam.py

5. Notes​

  • Classification models output class probabilities
  • Suitable for product recognition and defect classification
  • For localization tasks, use YOLO detection models

Maintained by HemiHex for Jetson-based advanced vision workflows.