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# vit-msn-small-beta-fia-manually-enhanced-HSV_test_5

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: 0.3267
- Accuracy: 0.9167

## 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: 5
- total_train_batch_size: 320
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.7143  | 1    | 1.1106          | 0.2292   |
| No log        | 1.4286  | 2    | 1.0984          | 0.2569   |
| No log        | 2.8571  | 4    | 1.0400          | 0.4097   |
| No log        | 3.5714  | 5    | 0.9960          | 0.5486   |
| No log        | 5.0     | 7    | 0.8868          | 0.7292   |
| No log        | 5.7143  | 8    | 0.8263          | 0.7778   |
| No log        | 6.4286  | 9    | 0.7651          | 0.8056   |
| 0.9808        | 7.8571  | 11   | 0.6521          | 0.8125   |
| 0.9808        | 8.5714  | 12   | 0.6052          | 0.8125   |
| 0.9808        | 10.0    | 14   | 0.5388          | 0.8125   |
| 0.9808        | 10.7143 | 15   | 0.5174          | 0.8125   |
| 0.9808        | 11.4286 | 16   | 0.5032          | 0.8125   |
| 0.9808        | 12.8571 | 18   | 0.5022          | 0.8125   |
| 0.9808        | 13.5714 | 19   | 0.5044          | 0.8194   |
| 0.5431        | 15.0    | 21   | 0.4773          | 0.8264   |
| 0.5431        | 15.7143 | 22   | 0.4439          | 0.8333   |
| 0.5431        | 16.4286 | 23   | 0.4198          | 0.8403   |
| 0.5431        | 17.8571 | 25   | 0.3873          | 0.8819   |
| 0.5431        | 18.5714 | 26   | 0.3730          | 0.8889   |
| 0.5431        | 20.0    | 28   | 0.3774          | 0.9028   |
| 0.5431        | 20.7143 | 29   | 0.3705          | 0.9097   |
| 0.4028        | 21.4286 | 30   | 0.3587          | 0.9097   |
| 0.4028        | 22.8571 | 32   | 0.3662          | 0.8958   |
| 0.4028        | 23.5714 | 33   | 0.3779          | 0.8681   |
| 0.4028        | 25.0    | 35   | 0.4322          | 0.8264   |
| 0.4028        | 25.7143 | 36   | 0.3944          | 0.8333   |
| 0.4028        | 26.4286 | 37   | 0.3585          | 0.8889   |
| 0.4028        | 27.8571 | 39   | 0.3608          | 0.8889   |
| 0.3497        | 28.5714 | 40   | 0.3972          | 0.8472   |
| 0.3497        | 30.0    | 42   | 0.3805          | 0.8611   |
| 0.3497        | 30.7143 | 43   | 0.3611          | 0.8819   |
| 0.3497        | 31.4286 | 44   | 0.3267          | 0.9167   |
| 0.3497        | 32.8571 | 46   | 0.3403          | 0.9028   |
| 0.3497        | 33.5714 | 47   | 0.3751          | 0.875    |
| 0.3497        | 35.0    | 49   | 0.3801          | 0.8681   |
| 0.3278        | 35.7143 | 50   | 0.3499          | 0.8958   |
| 0.3278        | 36.4286 | 51   | 0.3384          | 0.8958   |
| 0.3278        | 37.8571 | 53   | 0.3642          | 0.8542   |
| 0.3278        | 38.5714 | 54   | 0.3997          | 0.8194   |
| 0.3278        | 40.0    | 56   | 0.3843          | 0.8403   |
| 0.3278        | 40.7143 | 57   | 0.3676          | 0.8681   |
| 0.3278        | 41.4286 | 58   | 0.3464          | 0.9028   |
| 0.3334        | 42.8571 | 60   | 0.3618          | 0.8819   |
| 0.3334        | 43.5714 | 61   | 0.4006          | 0.8194   |
| 0.3334        | 45.0    | 63   | 0.4931          | 0.7639   |
| 0.3334        | 45.7143 | 64   | 0.4845          | 0.7708   |
| 0.3334        | 46.4286 | 65   | 0.4485          | 0.7917   |
| 0.3334        | 47.8571 | 67   | 0.3783          | 0.8472   |
| 0.3334        | 48.5714 | 68   | 0.3723          | 0.8472   |
| 0.3334        | 50.0    | 70   | 0.4077          | 0.8125   |
| 0.3334        | 50.7143 | 71   | 0.4381          | 0.7986   |
| 0.3334        | 51.4286 | 72   | 0.4627          | 0.7847   |
| 0.3334        | 52.8571 | 74   | 0.4445          | 0.7986   |
| 0.3334        | 53.5714 | 75   | 0.4141          | 0.8125   |
| 0.3334        | 55.0    | 77   | 0.3489          | 0.8681   |
| 0.3334        | 55.7143 | 78   | 0.3371          | 0.8958   |
| 0.3334        | 56.4286 | 79   | 0.3358          | 0.8889   |
| 0.3105        | 57.8571 | 81   | 0.3539          | 0.8681   |
| 0.3105        | 58.5714 | 82   | 0.3678          | 0.8542   |
| 0.3105        | 60.0    | 84   | 0.3931          | 0.8264   |
| 0.3105        | 60.7143 | 85   | 0.3938          | 0.8264   |
| 0.3105        | 61.4286 | 86   | 0.3897          | 0.8472   |
| 0.3105        | 62.8571 | 88   | 0.3638          | 0.8611   |
| 0.3105        | 63.5714 | 89   | 0.3496          | 0.875    |
| 0.3061        | 65.0    | 91   | 0.3305          | 0.8958   |
| 0.3061        | 65.7143 | 92   | 0.3284          | 0.9028   |
| 0.3061        | 66.4286 | 93   | 0.3284          | 0.8958   |
| 0.3061        | 67.8571 | 95   | 0.3337          | 0.8958   |
| 0.3061        | 68.5714 | 96   | 0.3374          | 0.8889   |
| 0.3061        | 70.0    | 98   | 0.3442          | 0.875    |
| 0.3061        | 70.7143 | 99   | 0.3452          | 0.875    |
| 0.3137        | 71.4286 | 100  | 0.3460          | 0.875    |


### Framework versions

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

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