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# msi-swinv2-tiny

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7208
- Accuracy: 0.6404
- F1: 0.5017
- Precision: 0.6290
- Recall: 0.4172

## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4992        | 1.0   | 2015 | 0.7072          | 0.6189   | 0.4517 | 0.6009    | 0.3619 |
| 0.4581        | 2.0   | 4031 | 0.7145          | 0.6383   | 0.4787 | 0.6387    | 0.3828 |
| 0.4229        | 3.0   | 6047 | 0.7146          | 0.6434   | 0.5077 | 0.6329    | 0.4238 |
| 0.4096        | 4.0   | 8060 | 0.7208          | 0.6404   | 0.5017 | 0.6290    | 0.4172 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

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