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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
- f1
model-index:
- name: stool-condition-classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: stool-image
type: generator
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8171064604185623
- name: F1
type: f1
value: 0.7841031149301826
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stool-condition-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the stool-image dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4538
- Auroc: 0.8897
- Accuracy: 0.8171
- Sensitivity: 0.8111
- Specificty: 0.8213
- F1: 0.7841
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Auroc | Accuracy | Sensitivity | Specificty | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:-----------:|:----------:|:------:|
| 0.5303 | 0.98 | 100 | 0.4327 | 0.8826 | 0.7942 | 0.7191 | 0.8607 | 0.7665 |
| 0.3909 | 1.96 | 200 | 0.5196 | 0.8675 | 0.8047 | 0.8539 | 0.7612 | 0.8042 |
| 0.5328 | 2.94 | 300 | 0.4421 | 0.8864 | 0.8074 | 0.7528 | 0.8557 | 0.7859 |
| 0.4834 | 3.92 | 400 | 0.4721 | 0.8596 | 0.7757 | 0.7135 | 0.8308 | 0.7493 |
| 0.4209 | 4.9 | 500 | 0.4797 | 0.8625 | 0.7863 | 0.6798 | 0.8806 | 0.7492 |
| 0.4567 | 5.88 | 600 | 0.5150 | 0.8688 | 0.7942 | 0.6011 | 0.9652 | 0.7329 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|