Instructions to use MoaazTalab/ViT_L16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoaazTalab/ViT_L16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MoaazTalab/ViT_L16") 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("MoaazTalab/ViT_L16") model = AutoModelForImageClassification.from_pretrained("MoaazTalab/ViT_L16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a5e9886540cc18b74d21b26bf955e10ff64e7b1c9f512c195e0c01e6fd1903d
- Size of remote file:
- 5.14 kB
- SHA256:
- 459ae50093485df6de25fbad1d63b321bd8f6f371a46c2f38fdb6d2d902d243a
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