Instructions to use NbAiLab/vit-front-page-384-complete-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/vit-front-page-384-complete-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="NbAiLab/vit-front-page-384-complete-v2") 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("NbAiLab/vit-front-page-384-complete-v2") model = AutoModelForImageClassification.from_pretrained("NbAiLab/vit-front-page-384-complete-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 616a9215084e9f5c02da6a8f8cbf39c566ebb47894989bf637cf2d93e5398856
- Size of remote file:
- 344 MB
- SHA256:
- e7aeae6401eee261a920c4636609b82fc52762b4b566ab785089a4bb27c1d0cb
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