Image Classification

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Demonstrating Ultralytics Image Classification effects on images, videos, and real-time detection using Python.

1. Model Introduction

Image classification is the simplest of the three tasks, involving assigning an entire image to one class from a set of predefined categories.

The output of an image classifier is a single class label and a confidence score. Image classification is very useful when you only need to know which category an image belongs to, without needing to know the location or exact shape of the object.

2. Image Classification: Image

Use yolo26n-cls.pt to predict images in the ultralytics project (non-ultralytics built-in images).

Navigate to the code folder:

cd /home/pi/ultralytics/ultralytics/yahboom_demo_26

Run the code:

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python3 04.classification_image.py

Preview

yolo recognition output image location: /home/pi/ultralytics/ultralytics/output/

Sample code:

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from ultralytics import YOLO

# Load a model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo26n-cls.pt")

# Run batched inference on a list of images
results = model("/home/pi/ultralytics/ultralytics/assets/dog.jpg")  # return a list of Results objects

# Process results list
for result in results:
    # boxes = result.boxes  # Boxes object for bounding box outputs
    # masks = result.masks  # Masks object for segmentation masks outputs
    # keypoints = result.keypoints  # Keypoints object for pose outputs
    probs = result.probs  # Probs object for classification outputs
    # obb = result.obb  # Oriented boxes object for OBB outputs
    result.show()  # display to screen
    result.save(filename="/home/pi/ultralytics/ultralytics/output/dog_output.jpg")  # save to disk

3. Image Classification: Video

Use yolo26n-cls.pt to predict videos in the ultralytics project (non-ultralytics built-in videos).

Navigate to the code folder:

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cd /home/pi/ultralytics/ultralytics/yahboom_demo_26

Run the code:

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python3 04.classification_video.py

Preview

yolo recognition output video location: /home/pi/ultralytics/ultralytics/output/

Sample code:

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import cv2
from ultralytics import YOLO

# Load the YOLO model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo26n-cls.pt")

# Open the video file
video_path = "/home/pi/ultralytics/ultralytics/videos/cup.mp4"
cap = cv2.VideoCapture(video_path)

# Get the video frame size and frame rate
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))

# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/pi/ultralytics/ultralytics/output/04.cup_output.mp4"
fou
 Pose Estimation
Oriented Bounding Boxes Object Detection