Transformers image processing examples¶

Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Credits: Huggingface documentation

Image classification¶

In [1]:

from transformers import pipeline

In [2]:

# Create the image classification pipeline with GPU
vision_classifier = pipeline(model="google/vit-base-patch16-224", device=0)  # Specify the GPU device index

Downloading (…)lve/main/config.json:   0%|          | 0.00/69.7k [00:00<?, ?B/s]

Downloading pytorch_model.bin:   0%|          | 0.00/346M [00:00<?, ?B/s]

Downloading (…)rocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]

In [3]:

image_url="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"

In [4]:

from IPython.display import Image
Image(url=image_url)

Out[4]:

In [5]:

preds = vision_classifier(image_url)
preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
preds

Out[5]:

[{'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'}]

In [ ]: