Instructions to use tangg555/clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tangg555/clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tangg555/clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("tangg555/clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist") model = AutoModelForImageClassification.from_pretrained("tangg555/clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f80a2e34f0bda36b0f0500c0c36156e25ce49c0078e1292b9ef7e2e35433d73
- Size of remote file:
- 5.24 kB
- SHA256:
- 393884f7a2ab57d0ea15acc09712195520784dff56707a69e60ddad8a000cb76
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