# Load model directly
from transformers import AutoTokenizer, AutoModelForImageClassification
tokenizer = AutoTokenizer.from_pretrained("addy88/perceiver_image_classifier")
model = AutoModelForImageClassification.from_pretrained("addy88/perceiver_image_classifier")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
How to use
Here is how to use this model in PyTorch:
from transformers import PerceiverFeatureExtractor, PerceiverForImageClassificationLearned
import requests
from PIL import Image
feature_extractor = PerceiverFeatureExtractor.from_pretrained("addy88/perceiver_image_classifier")
model = PerceiverForImageClassificationLearned.from_pretrained("addy88/perceiver_image_classifier")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# prepare input
encoding = feature_extractor(image, return_tensors="pt")
inputs = encoding.pixel_values
# forward pass
outputs = model(inputs)
logits = outputs.logits
print("Predicted class:", model.config.id2label[logits.argmax(-1).item()])
>>> should print Predicted class: tabby, tabby cat
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="addy88/perceiver_image_classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")