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# vit-msn-small-beta-fia-manually-enhanced_test_1

This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7014
- Accuracy: 0.7465

## 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: 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: 0.2
- num_epochs: 50
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.5714  | 1    | 0.9554          | 0.3732   |
| No log        | 1.7143  | 3    | 0.9304          | 0.5211   |
| No log        | 2.8571  | 5    | 0.8979          | 0.6338   |
| No log        | 4.0     | 7    | 0.8770          | 0.6479   |
| No log        | 4.5714  | 8    | 0.8595          | 0.6408   |
| 0.877         | 5.7143  | 10   | 0.8456          | 0.5634   |
| 0.877         | 6.8571  | 12   | 0.8825          | 0.4859   |
| 0.877         | 8.0     | 14   | 0.8294          | 0.5563   |
| 0.877         | 8.5714  | 15   | 0.7883          | 0.6197   |
| 0.877         | 9.7143  | 17   | 0.7541          | 0.6549   |
| 0.877         | 10.8571 | 19   | 0.7689          | 0.6690   |
| 0.7053        | 12.0    | 21   | 0.7652          | 0.6620   |
| 0.7053        | 12.5714 | 22   | 0.7496          | 0.6690   |
| 0.7053        | 13.7143 | 24   | 0.7139          | 0.7183   |
| 0.7053        | 14.8571 | 26   | 0.7014          | 0.7465   |
| 0.7053        | 16.0    | 28   | 0.7290          | 0.7183   |
| 0.7053        | 16.5714 | 29   | 0.7431          | 0.6901   |
| 0.6176        | 17.7143 | 31   | 0.7498          | 0.6690   |
| 0.6176        | 18.8571 | 33   | 0.7439          | 0.6761   |
| 0.6176        | 20.0    | 35   | 0.7347          | 0.6972   |
| 0.6176        | 20.5714 | 36   | 0.7377          | 0.6901   |
| 0.6176        | 21.7143 | 38   | 0.7227          | 0.6901   |
| 0.6053        | 22.8571 | 40   | 0.7228          | 0.7042   |
| 0.6053        | 24.0    | 42   | 0.7282          | 0.6901   |
| 0.6053        | 24.5714 | 43   | 0.7363          | 0.6761   |
| 0.6053        | 25.7143 | 45   | 0.7431          | 0.6831   |
| 0.6053        | 26.8571 | 47   | 0.7450          | 0.6831   |
| 0.6053        | 28.0    | 49   | 0.7455          | 0.6761   |
| 0.5926        | 28.5714 | 50   | 0.7447          | 0.6761   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1

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