Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use tadeous/vit-model-beimer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tadeous/vit-model-beimer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tadeous/vit-model-beimer") 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("tadeous/vit-model-beimer") model = AutoModelForImageClassification.from_pretrained("tadeous/vit-model-beimer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c22842b8cb8310ea0433917460e77b78f054c44129d965cc7cf6c84458d93bf9
- Size of remote file:
- 343 MB
- SHA256:
- 7f2ecb6bdbf8fdc811f0b6bfbf844c4285e0f90426a86d17763343fefe4cd498
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