Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use Thamer/beit_large512_fine_tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thamer/beit_large512_fine_tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thamer/beit_large512_fine_tuned") 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("Thamer/beit_large512_fine_tuned") model = AutoModelForImageClassification.from_pretrained("Thamer/beit_large512_fine_tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a4c64133ebccaae98d35aeb9804a6424900dd1cf980111bafc2f50e6f81dad7
- Size of remote file:
- 347 MB
- SHA256:
- 47066ac1a34dacf85f053c05bf68ff184c16697a6b25db8a6b6b9e01a419b33f
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