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-60-20-20 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-60-20-20 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-60-20-20") 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-60-20-20") model = AutoModelForImageClassification.from_pretrained("ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-60-20-20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 4.97, | |
| "eval_accuracy": 1.0, | |
| "eval_loss": 0.00011626419291133061, | |
| "eval_runtime": 119.1378, | |
| "eval_samples_per_second": 25.181, | |
| "eval_steps_per_second": 1.578 | |
| } |