---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: VANBase-finetuned-brs-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: 0.5882352941176471
    - name: F1
      type: f1
      value: 0.6956521739130435
---

<!-- 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. -->

# VANBase-finetuned-brs-finetuned-brs

This model is a fine-tuned version of [Visual-Attention-Network/van-base](https://huggingface.co./Visual-Attention-Network/van-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7056
- Accuracy: 0.5882
- F1: 0.6957
- Precision (ppv): 0.6154
- Recall (sensitivity): 0.8
- Specificity: 0.2857
- Npv: 0.5
- Auc: 0.5429

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.6589        | 6.25  | 100  | 0.6655          | 0.5882   | 0.6316 | 0.6667          | 0.6                  | 0.5714      | 0.5    | 0.5857 |
| 0.6262        | 12.49 | 200  | 0.6917          | 0.5294   | 0.6364 | 0.5833          | 0.7                  | 0.2857      | 0.4    | 0.4929 |
| 0.4706        | 18.74 | 300  | 0.6776          | 0.5882   | 0.6957 | 0.6154          | 0.8                  | 0.2857      | 0.5    | 0.5429 |
| 0.5202        | 24.98 | 400  | 0.7018          | 0.5294   | 0.6    | 0.6             | 0.6                  | 0.4286      | 0.4286 | 0.5143 |
| 0.4628        | 31.25 | 500  | 0.6903          | 0.6471   | 0.75   | 0.6429          | 0.9                  | 0.2857      | 0.6667 | 0.5929 |
| 0.3525        | 37.49 | 600  | 0.7241          | 0.5294   | 0.6667 | 0.5714          | 0.8                  | 0.1429      | 0.3333 | 0.4714 |
| 0.2877        | 43.74 | 700  | 0.8262          | 0.5882   | 0.7407 | 0.5882          | 1.0                  | 0.0         | nan    | 0.5    |
| 0.2921        | 49.98 | 800  | 0.8058          | 0.4706   | 0.64   | 0.5333          | 0.8                  | 0.0         | 0.0    | 0.4    |
| 0.3834        | 56.25 | 900  | 0.7864          | 0.5882   | 0.7407 | 0.5882          | 1.0                  | 0.0         | nan    | 0.5    |
| 0.2267        | 62.49 | 1000 | 0.5520          | 0.7647   | 0.8182 | 0.75            | 0.9                  | 0.5714      | 0.8    | 0.7357 |
| 0.3798        | 68.74 | 1100 | 0.8722          | 0.4706   | 0.64   | 0.5333          | 0.8                  | 0.0         | 0.0    | 0.4    |
| 0.2633        | 74.98 | 1200 | 0.7260          | 0.6471   | 0.7273 | 0.6667          | 0.8                  | 0.4286      | 0.6    | 0.6143 |
| 0.3439        | 81.25 | 1300 | 1.0187          | 0.4118   | 0.5455 | 0.5             | 0.6                  | 0.1429      | 0.2    | 0.3714 |
| 0.2532        | 87.49 | 1400 | 0.8812          | 0.5882   | 0.7407 | 0.5882          | 1.0                  | 0.0         | nan    | 0.5    |
| 0.0841        | 93.74 | 1500 | 0.8717          | 0.5294   | 0.6923 | 0.5625          | 0.9                  | 0.0         | 0.0    | 0.45   |
| 0.3409        | 99.98 | 1600 | 0.7056          | 0.5882   | 0.6957 | 0.6154          | 0.8                  | 0.2857      | 0.5    | 0.5429 |


### Framework versions

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