---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: dit-base-finetuned-brs
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8823529411764706
    - name: F1
      type: f1
      value: 0.8571428571428571
---

<!-- 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. -->

# dit-base-finetuned-brs

This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8748
- Accuracy: 0.8824
- F1: 0.8571
- Precision (ppv): 0.8571
- Recall (sensitivity): 0.8571
- Specificity: 0.9
- Npv: 0.9
- Auc: 0.8786

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision (ppv) | Recall (sensitivity) | Specificity | Npv    | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.6624        | 6.25  | 100  | 0.5548          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.5201        | 12.49 | 200  | 0.4617          | 0.8824   | 0.8571 | 0.8571          | 0.8571               | 0.9         | 0.9    | 0.8786 |
| 0.5172        | 18.74 | 300  | 0.4249          | 0.8235   | 0.8000 | 0.75            | 0.8571               | 0.8         | 0.8889 | 0.8286 |
| 0.4605        | 24.98 | 400  | 0.3172          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.4894        | 31.25 | 500  | 0.4466          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.3694        | 37.49 | 600  | 0.5077          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.6172        | 43.74 | 700  | 0.5722          | 0.7647   | 0.7143 | 0.7143          | 0.7143               | 0.8         | 0.8    | 0.7571 |
| 0.3671        | 49.98 | 800  | 0.7006          | 0.7647   | 0.6667 | 0.8             | 0.5714               | 0.9         | 0.75   | 0.7357 |
| 0.4109        | 56.25 | 900  | 0.4410          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.3198        | 62.49 | 1000 | 0.7226          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.4283        | 68.74 | 1100 | 0.8089          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.3273        | 74.98 | 1200 | 0.9059          | 0.7647   | 0.6667 | 0.8             | 0.5714               | 0.9         | 0.75   | 0.7357 |
| 0.3237        | 81.25 | 1300 | 0.8520          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
| 0.2014        | 87.49 | 1400 | 0.9183          | 0.7647   | 0.6667 | 0.8             | 0.5714               | 0.9         | 0.75   | 0.7357 |
| 0.3204        | 93.74 | 1500 | 0.6769          | 0.8824   | 0.8571 | 0.8571          | 0.8571               | 0.9         | 0.9    | 0.8786 |
| 0.1786        | 99.98 | 1600 | 0.8748          | 0.8824   | 0.8571 | 0.8571          | 0.8571               | 0.9         | 0.9    | 0.8786 |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1