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
File size: 500 Bytes
aa26950 ec5c089 eab3421 ec5c089 aa26950 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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
} |