Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use maurope/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maurope/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="maurope/vit_model") 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("maurope/vit_model") model = AutoModelForImageClassification.from_pretrained("maurope/vit_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73387200ea39b93deadddcb7a0ec720105944d101bbe47051dd1cc339ee8c8c3
- Size of remote file:
- 3.9 kB
- SHA256:
- 160e1159b746bbbd0cbaf98112579389145cf34fe33d80c2ca40a5ef02803300
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