Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use 02shanky/test_model_graphics_classification_LION with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 02shanky/test_model_graphics_classification_LION with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="02shanky/test_model_graphics_classification_LION") 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("02shanky/test_model_graphics_classification_LION") model = AutoModelForImageClassification.from_pretrained("02shanky/test_model_graphics_classification_LION", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c54dc03cb4311face12666252f043b76ad9fc9ccde50b90777c4cdeb0f23f8d
- Size of remote file:
- 3.96 kB
- SHA256:
- 6f046f9895f1722bbcc3e66568be4f0e40c32f47ba048ea803704299ff0a3522
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