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
File size: 206 Bytes
bf5ddc8 | 1 2 3 4 5 6 7 8 | {
"epoch": 4.96,
"eval_accuracy": 0.9995570321151717,
"eval_loss": 0.001114627462811768,
"eval_runtime": 195.9028,
"eval_samples_per_second": 23.047,
"eval_steps_per_second": 1.445
} |