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:
- 0f85ab0de6feacf8ecf52a1f588714ef6672b1608fade5632296db30d3443ff8
- Size of remote file:
- 3.96 kB
- SHA256:
- 01df3b60b3412b00b3cd89ca73e1ff99c588268fe25909e51c48a5d746549639
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.