---
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