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Like most classification tasks, there are many practical use cases for image classification, some of which include:
healthcare: label medical images to detect disease or monitor patient health
environment: label satellite images to monitor deforestation, inform wildland management or detect wildfires
agriculture: label images of crops to monitor plant health or satellite images for land use monitoring
ecology: label images of animal or plant species to monitor wildlife populations or track endangered species
from transformers import pipeline
classifier = pipeline(task="image-classification")
preds = classifier(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
)
preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
print(*preds, sep="\n")
{'score': 0.4335, 'label': 'lynx, catamount'}
{'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
{'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}
{'score': 0.0239, 'label': 'Egyptian cat'}
{'score': 0.0229, 'label': 'tiger cat'}
Object detection
Unlike image classification, object detection identifies multiple objects within an image and the objects' positions in an image (defined by the bounding box). |