
<!-- 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. -->

# pvt-tiny-224

This model is a fine-tuned version of [Zetatech/pvt-tiny-224](https://huggingface.co/Zetatech/pvt-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4869
- Accuracy: 0.7833
- Precision: 0.7681
- Recall: 0.7833
- F1 Score: 0.7632

## 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    | 0.5984          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 2.0   | 8    | 0.6103          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 3.0   | 12   | 0.5861          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 4.0   | 16   | 0.5478          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 5.0   | 20   | 0.5961          | 0.725    | 0.7119    | 0.725  | 0.7171   |
| No log        | 6.0   | 24   | 0.5317          | 0.7542   | 0.7261    | 0.7542 | 0.7159   |
| No log        | 7.0   | 28   | 0.5620          | 0.7458   | 0.7289    | 0.7458 | 0.7342   |
| 0.5878        | 8.0   | 32   | 0.5281          | 0.7542   | 0.7316    | 0.7542 | 0.6973   |
| 0.5878        | 9.0   | 36   | 0.5434          | 0.7625   | 0.7395    | 0.7625 | 0.7368   |
| 0.5878        | 10.0  | 40   | 0.5236          | 0.775    | 0.7658    | 0.775  | 0.7321   |
| 0.5878        | 11.0  | 44   | 0.5411          | 0.7542   | 0.7382    | 0.7542 | 0.7429   |
| 0.5878        | 12.0  | 48   | 0.5186          | 0.7708   | 0.7507    | 0.7708 | 0.7460   |
| 0.5878        | 13.0  | 52   | 0.5194          | 0.7667   | 0.7500    | 0.7667 | 0.7533   |
| 0.5878        | 14.0  | 56   | 0.5049          | 0.7875   | 0.7739    | 0.7875 | 0.7621   |
| 0.4973        | 15.0  | 60   | 0.5125          | 0.7833   | 0.7691    | 0.7833 | 0.7709   |
| 0.4973        | 16.0  | 64   | 0.5000          | 0.7917   | 0.7804    | 0.7917 | 0.7656   |
| 0.4973        | 17.0  | 68   | 0.5137          | 0.7583   | 0.7560    | 0.7583 | 0.7571   |
| 0.4973        | 18.0  | 72   | 0.4833          | 0.8      | 0.788     | 0.8    | 0.7833   |
| 0.4973        | 19.0  | 76   | 0.4929          | 0.7917   | 0.7816    | 0.7917 | 0.7843   |
| 0.4973        | 20.0  | 80   | 0.4858          | 0.8042   | 0.7930    | 0.8042 | 0.7887   |
| 0.4973        | 21.0  | 84   | 0.4900          | 0.7917   | 0.7777    | 0.7917 | 0.7743   |
| 0.4973        | 22.0  | 88   | 0.4886          | 0.7958   | 0.7829    | 0.7958 | 0.7815   |
| 0.439         | 23.0  | 92   | 0.4841          | 0.7917   | 0.7778    | 0.7917 | 0.7723   |
| 0.439         | 24.0  | 96   | 0.4855          | 0.8      | 0.7883    | 0.8    | 0.7885   |
| 0.439         | 25.0  | 100  | 0.4856          | 0.8      | 0.7879    | 0.8    | 0.7869   |
| 0.439         | 26.0  | 104  | 0.4839          | 0.8      | 0.7879    | 0.8    | 0.7869   |
| 0.439         | 27.0  | 108  | 0.4811          | 0.8      | 0.7879    | 0.8    | 0.7869   |
| 0.439         | 28.0  | 112  | 0.4834          | 0.8      | 0.7889    | 0.8    | 0.7901   |
| 0.439         | 29.0  | 116  | 0.4839          | 0.8      | 0.7889    | 0.8    | 0.7901   |
| 0.4092        | 30.0  | 120  | 0.4838          | 0.8      | 0.7889    | 0.8    | 0.7901   |


### Framework versions

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

normal abnormal