
<!-- 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. -->

# Swin_transformer_dent_detection

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6854
- Accuracy: 0.55
- F1: 0.55
- Recall: 0.55
- Precision: 0.55

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1   | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:------:|:---------:|
| No log        | 0.8   | 1    | 0.6854          | 0.55     | 0.55 | 0.55   | 0.55      |
| No log        | 1.8   | 2    | 0.9226          | 0.55     | 0.55 | 0.55   | 0.55      |
| No log        | 2.8   | 3    | 0.8610          | 0.55     | 0.55 | 0.55   | 0.55      |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1

dent perfect