| 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). |