Classify an Object in an Image in Python Using the YOLO Model

To perform object classification on an image file using Python, we can use the open source pre-trained YOLO model from Ultralytics.

First, install the library using:

$ pip install ultralytics

For example, assume we have an image of a tractor in a local file tractor.jpeg under images/.

Note that we can also run the model from the command line using:

$ yolo predict source='images/tractor.jpeg'

In Python, we need to extract the result from all of the model output, which requires a bit more code.

The model’s predict function will return a list of results with probability values, as well as a list of all labels.

The code below will extract the highest probability label and print it.

from ultralytics import YOLO

model = YOLO("yolov8n-cls.pt")

# Path to an image file assumed to exist.
results = model.predict("images/tractor.jpeg")

# Overall results is a list.
result = results[0]

probabilities = result.probs

# Top1 is the most likely result.
topLabelNumber = probabilities.top1

# Now find the label name for that label number.
allNames = result.names
for labelNumber, label in allNames.items():
  if labelNumber == topLabelNumber:
    resultLabel = label

print("Classification result:")
print(resultLabel)