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
| [project] | |
| name = "image-classification-model" | |
| version = "0.1.0" | |
| description = "MNIST-compatible image classification model" | |
| requires-python = ">=3.8" | |
| dependencies = [ | |
| "torch>=2.0.0", | |
| "transformers>=4.30.0", | |
| "Pillow>=9.0.0", | |
| "datasets>=2.0.0", | |
| "accelerate>=0.26.0" | |
| ] | |
| [build-system] | |
| requires = ["setuptools>=65.5.1", "wheel"] | |
| build-backend = "setuptools.build_meta" | |
| [tool.setuptools] | |
| packages = ["image_classification_model"] | |
| [tool.setuptools.package-dir] | |
| image_classification_model = "src" | |
| [tool.setuptools.entry_points] | |
| console_scripts = [ | |
| "train = bin.train:main", | |
| "subtract = bin.subtract:main" | |
| ] |