Instructions to use Thouph/clip-vit-l-224-patch14-datacomp-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thouph/clip-vit-l-224-patch14-datacomp-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thouph/clip-vit-l-224-patch14-datacomp-image-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("Thouph/clip-vit-l-224-patch14-datacomp-image-classification") model = AutoModelForImageClassification.from_pretrained("Thouph/clip-vit-l-224-patch14-datacomp-image-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b957507c87e505fca6e5f1b09bd8b4d15d7621a89760b8d2c34bfaf2da9fb55
- Size of remote file:
- 1.24 GB
- SHA256:
- f55ccff6c835d2d6ade6f65b59524755289562a3c732706fd69f14b81885a468
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