Instructions to use SupremoUGH/image-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SupremoUGH/image-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SupremoUGH/image-classification-model") 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("SupremoUGH/image-classification-model") model = AutoModelForImageClassification.from_pretrained("SupremoUGH/image-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 294 Bytes
ab8b628 | 1 2 3 4 5 6 7 8 9 10 11 | from transformers import ViTImageProcessor
from .utils import MODEL_DIR
processor = ViTImageProcessor.from_pretrained(MODEL_DIR)
def preprocess_image(image):
"""Preprocesses a single image for ViT inference."""
inputs = processor(images=image, return_tensors="pt")
return inputs
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