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
File size: 539 Bytes
aa26793 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"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
} |