---
license: apache-2.0
base_model: google/vit-base-patch16-224
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: 0.941747572815534
    - name: F1
      type: f1
      value: 0.9302325581395349
---

<!-- 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](https://huggingface.co/google/vit-base-patch16-224) on the stool-image dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4237
- Auroc: 0.9418
- Accuracy: 0.9417
- Sensitivity: 0.9091
- Specificty: 0.9661
- Ppv: 0.9524
- Npv: 0.9344
- F1: 0.9302
- Model Selection: 0.9215

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Auroc  | Accuracy | Sensitivity | Specificty | Ppv    | Npv    | F1     | Model Selection |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:-----------:|:----------:|:------:|:------:|:------:|:---------------:|
| 0.5076        | 0.98  | 100  | 0.5361          | 0.8538 | 0.7731   | 0.5393      | 0.9801     | 0.96   | 0.7061 | 0.6906 | 0.5592          |
| 0.4086        | 1.96  | 200  | 0.4857          | 0.8728 | 0.7836   | 0.6011      | 0.9453     | 0.9068 | 0.7280 | 0.7230 | 0.6558          |
| 0.5208        | 2.94  | 300  | 0.5109          | 0.8059 | 0.7599   | 0.6124      | 0.8905     | 0.8321 | 0.7218 | 0.7055 | 0.7218          |
| 0.474         | 3.92  | 400  | 0.5212          | 0.8601 | 0.7995   | 0.6180      | 0.9602     | 0.9322 | 0.7395 | 0.7432 | 0.6578          |
| 0.4285        | 4.9   | 500  | 0.4511          | 0.8728 | 0.7757   | 0.7472      | 0.8010     | 0.7688 | 0.7816 | 0.7578 | 0.9462          |
| 0.3506        | 5.88  | 600  | 0.4716          | 0.8691 | 0.8047   | 0.6798      | 0.9154     | 0.8768 | 0.7635 | 0.7658 | 0.7644          |
| 0.4239        | 6.86  | 700  | 0.5043          | 0.8517 | 0.8100   | 0.6685      | 0.9353     | 0.9015 | 0.7611 | 0.7677 | 0.7332          |
| 0.2447        | 7.84  | 800  | 0.5804          | 0.8592 | 0.8074   | 0.6910      | 0.9104     | 0.8723 | 0.7689 | 0.7712 | 0.7806          |
| 0.1739        | 8.82  | 900  | 0.6225          | 0.8562 | 0.8074   | 0.7135      | 0.8905     | 0.8523 | 0.7783 | 0.7768 | 0.8229          |
| 0.2888        | 9.8   | 1000 | 0.5807          | 0.8570 | 0.8047   | 0.7528      | 0.8507     | 0.8171 | 0.7953 | 0.7836 | 0.9021          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.0.1
- Datasets 2.14.7
- Tokenizers 0.15.2