Instructions to use Pranjal12345/Classification_Transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pranjal12345/Classification_Transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Pranjal12345/Classification_Transformers") 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("Pranjal12345/Classification_Transformers") model = AutoModelForImageClassification.from_pretrained("Pranjal12345/Classification_Transformers") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d9548e0a708c5cad4b12e6626adf1d4ccb7400579a3ba1180462102de5c0781
- Size of remote file:
- 343 MB
- SHA256:
- f6df89e32ecde72e730303f864de95dceb7ab8f5e7d736bc678981168e9c4a22
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