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
| import sys | |
| from image_classification_model.predict import predict | |
| def main(): | |
| if len(sys.argv) < 2: | |
| print("Usage: python predict.py <image_path>") | |
| sys.exit(1) | |
| image_path = sys.argv[1] | |
| # Run prediction (handles preprocessing internally) | |
| predicted_label = predict(image_path) | |
| # Print output in Hugging Face-compatible format | |
| print({"label": predicted_label}) | |
| if __name__ == "__main__": | |
| main() | |