Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30") 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("ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30") model = AutoModelForImageClassification.from_pretrained("ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 936b1990fe6494a520c86dc04a76e233e3ab233debddf187bb63cf4917ff8f20
- Size of remote file:
- 4.09 kB
- SHA256:
- f0e4b4c94d6aad6877a6cb9ac583d0a97a787f1beec3925d03480ede4f6b7207
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