
<!-- 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. -->

# swiftformer-xs

This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6833
- Accuracy: 0.57
- Precision: 0.5995
- Recall: 0.57
- F1 Score: 0.5828

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.6713          | 0.6292   | 0.6454    | 0.6292 | 0.6365   |
| No log        | 2.0   | 8    | 0.7142          | 0.475    | 0.6155    | 0.475  | 0.5020   |
| No log        | 3.0   | 12   | 0.7298          | 0.425    | 0.6026    | 0.425  | 0.4435   |
| No log        | 4.0   | 16   | 0.7389          | 0.4792   | 0.6408    | 0.4792 | 0.5023   |
| No log        | 5.0   | 20   | 0.7427          | 0.4792   | 0.6408    | 0.4792 | 0.5023   |
| No log        | 6.0   | 24   | 0.7235          | 0.5083   | 0.6424    | 0.5083 | 0.5348   |
| No log        | 7.0   | 28   | 0.6893          | 0.5875   | 0.6687    | 0.5875 | 0.6107   |
| 0.6981        | 8.0   | 32   | 0.6816          | 0.6042   | 0.6847    | 0.6042 | 0.6264   |
| 0.6981        | 9.0   | 36   | 0.6866          | 0.6042   | 0.6888    | 0.6042 | 0.6266   |
| 0.6981        | 10.0  | 40   | 0.7005          | 0.575    | 0.6751    | 0.575  | 0.5996   |
| 0.6981        | 11.0  | 44   | 0.7127          | 0.525    | 0.6554    | 0.525  | 0.5510   |
| 0.6981        | 12.0  | 48   | 0.7098          | 0.5333   | 0.6595    | 0.5333 | 0.5593   |
| 0.6981        | 13.0  | 52   | 0.7126          | 0.5208   | 0.6579    | 0.5208 | 0.5463   |
| 0.6981        | 14.0  | 56   | 0.7114          | 0.5292   | 0.6575    | 0.5292 | 0.5551   |
| 0.6656        | 15.0  | 60   | 0.6908          | 0.5667   | 0.6712    | 0.5667 | 0.5917   |
| 0.6656        | 16.0  | 64   | 0.6804          | 0.5833   | 0.6749    | 0.5833 | 0.6073   |
| 0.6656        | 17.0  | 68   | 0.6806          | 0.5958   | 0.6808    | 0.5958 | 0.6188   |
| 0.6656        | 18.0  | 72   | 0.6884          | 0.5583   | 0.6629    | 0.5583 | 0.5838   |
| 0.6656        | 19.0  | 76   | 0.6821          | 0.5708   | 0.6647    | 0.5708 | 0.5955   |
| 0.6656        | 20.0  | 80   | 0.6663          | 0.6042   | 0.6806    | 0.6042 | 0.6261   |
| 0.6656        | 21.0  | 84   | 0.6717          | 0.6      | 0.6787    | 0.6    | 0.6223   |
| 0.6656        | 22.0  | 88   | 0.6682          | 0.6083   | 0.6826    | 0.6083 | 0.6299   |
| 0.6443        | 23.0  | 92   | 0.6683          | 0.6167   | 0.6946    | 0.6167 | 0.6381   |
| 0.6443        | 24.0  | 96   | 0.6733          | 0.6      | 0.6911    | 0.6    | 0.6230   |
| 0.6443        | 25.0  | 100  | 0.6647          | 0.6083   | 0.6866    | 0.6083 | 0.6302   |
| 0.6443        | 26.0  | 104  | 0.6729          | 0.6083   | 0.6907    | 0.6083 | 0.6305   |
| 0.6443        | 27.0  | 108  | 0.6740          | 0.6042   | 0.6930    | 0.6042 | 0.6268   |
| 0.6443        | 28.0  | 112  | 0.6809          | 0.5917   | 0.6916    | 0.5917 | 0.6153   |
| 0.6443        | 29.0  | 116  | 0.6778          | 0.6042   | 0.7017    | 0.6042 | 0.6270   |
| 0.6313        | 30.0  | 120  | 0.6794          | 0.5958   | 0.6935    | 0.5958 | 0.6192   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

normal abnormal