Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use hossay/stool-condition-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hossay/stool-condition-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hossay/stool-condition-classification") 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("hossay/stool-condition-classification") model = AutoModelForImageClassification.from_pretrained("hossay/stool-condition-classification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.941747572815534, | |
| "eval_auroc": 0.941833590138675, | |
| "eval_f1": 0.9302325581395349, | |
| "eval_loss": 0.42368707060813904, | |
| "eval_model_selection": 0.9215271357644239, | |
| "eval_npv": 0.9344262295081968, | |
| "eval_ppv": 0.9523809523809523, | |
| "eval_runtime": 71.7641, | |
| "eval_samples_per_second": 15.314, | |
| "eval_sensitivity": 0.9090909090909091, | |
| "eval_specificty": 0.9661016949152542, | |
| "eval_steps_per_second": 1.923, | |
| "step": 1020 | |
| } |