Instructions to use Valencio/LLM_course_ViT_model_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Valencio/LLM_course_ViT_model_image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Valencio/LLM_course_ViT_model_image_classification") 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("Valencio/LLM_course_ViT_model_image_classification") model = AutoModelForImageClassification.from_pretrained("Valencio/LLM_course_ViT_model_image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bb8f91dc291a58ecc22d85bd8dedd5b9d53abac4d4ecb808cd39ded672aec96
- Size of remote file:
- 344 MB
- SHA256:
- 2578f87de3764a9effd0f82ada6dce1a3da88448f9156af7f8ad5554a90a8fe4
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