Image Classification
Transformers
TensorBoard
Safetensors
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use BilalMuftuoglu/beit-base-patch16-224-hasta-55-fold1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BilalMuftuoglu/beit-base-patch16-224-hasta-55-fold1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BilalMuftuoglu/beit-base-patch16-224-hasta-55-fold1") 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("BilalMuftuoglu/beit-base-patch16-224-hasta-55-fold1") model = AutoModelForImageClassification.from_pretrained("BilalMuftuoglu/beit-base-patch16-224-hasta-55-fold1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f3d754e5db365a29873c5339f6dfe6b714895fb7b90142f7c29c9d36f4c470d
- Size of remote file:
- 5.18 kB
- SHA256:
- 31b536f0f67feceba447821023fd51ab72c88bea356d8eb9dc5f7dcdbbbc8ab8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.