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
| 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 | |