Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis") 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("DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_Macro F1": 0.9364724676515609, | |
| "eval_Macro Precision": 0.9454970963639796, | |
| "eval_Macro Recall": 0.9375, | |
| "eval_Micro F1": 0.9375, | |
| "eval_Micro Precision": 0.9375, | |
| "eval_Micro Recall": 0.9375, | |
| "eval_Weighted F1": 0.936472467651561, | |
| "eval_Weighted Precision": 0.9454970963639796, | |
| "eval_Weighted Recall": 0.9375, | |
| "eval_accuracy": 0.9375, | |
| "eval_loss": 0.2538217604160309, | |
| "eval_runtime": 724.324, | |
| "eval_samples_per_second": 1.104, | |
| "eval_steps_per_second": 0.138 | |
| } |