Instructions to use akashmaggon/classification-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akashmaggon/classification-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="akashmaggon/classification-vit") 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("akashmaggon/classification-vit") model = AutoModelForImageClassification.from_pretrained("akashmaggon/classification-vit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- baff980bc0ae2099431eed9a3959addb978fe53f28a921d61731ef05bc5e218b
- Size of remote file:
- 4.6 kB
- SHA256:
- cd261b9dd18317aaac71e61e95a37bbeb5a96ba37693257aa8f02f36c4cd77fd
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