Instructions to use alicelouis/Swin2e-4Lion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alicelouis/Swin2e-4Lion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="alicelouis/Swin2e-4Lion") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("alicelouis/Swin2e-4Lion") model = AutoModelForImageClassification.from_pretrained("alicelouis/Swin2e-4Lion") - Notebooks
- Google Colab
- Kaggle
from transformers import AutoImageProcessor, SwinForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = AutoImageProcessor.from_pretrained("alicelouis/Swin2e-4Lion")
model = SwinForImageClassification.from_pretrained("alicelouis/Swin2e-4Lion")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
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