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# vit-msn-small-beta-fia-equally-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: 0.6061
- Accuracy: 0.8732

## 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: 100
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.5714  | 1    | 1.5578          | 0.0704   |
| No log        | 1.7143  | 3    | 1.4950          | 0.0634   |
| No log        | 2.8571  | 5    | 1.3574          | 0.0634   |
| No log        | 4.0     | 7    | 1.1698          | 0.1268   |
| No log        | 4.5714  | 8    | 1.0682          | 0.3169   |
| 1.5036        | 5.7143  | 10   | 0.8754          | 0.7958   |
| 1.5036        | 6.8571  | 12   | 0.7359          | 0.8239   |
| 1.5036        | 8.0     | 14   | 0.6782          | 0.8169   |
| 1.5036        | 8.5714  | 15   | 0.6718          | 0.8169   |
| 1.5036        | 9.7143  | 17   | 0.6821          | 0.8099   |
| 1.5036        | 10.8571 | 19   | 0.7157          | 0.8028   |
| 0.7486        | 12.0    | 21   | 0.7173          | 0.8099   |
| 0.7486        | 12.5714 | 22   | 0.6967          | 0.8169   |
| 0.7486        | 13.7143 | 24   | 0.6847          | 0.8169   |
| 0.7486        | 14.8571 | 26   | 0.6827          | 0.8239   |
| 0.7486        | 16.0    | 28   | 0.6959          | 0.8380   |
| 0.7486        | 16.5714 | 29   | 0.6826          | 0.8521   |
| 0.6547        | 17.7143 | 31   | 0.6360          | 0.8310   |
| 0.6547        | 18.8571 | 33   | 0.6257          | 0.8521   |
| 0.6547        | 20.0    | 35   | 0.6594          | 0.8732   |
| 0.6547        | 20.5714 | 36   | 0.6784          | 0.8380   |
| 0.6547        | 21.7143 | 38   | 0.6578          | 0.8521   |
| 0.5817        | 22.8571 | 40   | 0.6146          | 0.8592   |
| 0.5817        | 24.0    | 42   | 0.6212          | 0.8732   |
| 0.5817        | 24.5714 | 43   | 0.6395          | 0.8732   |
| 0.5817        | 25.7143 | 45   | 0.6452          | 0.8732   |
| 0.5817        | 26.8571 | 47   | 0.6317          | 0.8803   |
| 0.5817        | 28.0    | 49   | 0.6332          | 0.8803   |
| 0.5632        | 28.5714 | 50   | 0.6418          | 0.8732   |
| 0.5632        | 29.7143 | 52   | 0.6383          | 0.8803   |
| 0.5632        | 30.8571 | 54   | 0.6367          | 0.8592   |
| 0.5632        | 32.0    | 56   | 0.6253          | 0.8732   |
| 0.5632        | 32.5714 | 57   | 0.6268          | 0.8592   |
| 0.5632        | 33.7143 | 59   | 0.6234          | 0.8662   |
| 0.5328        | 34.8571 | 61   | 0.6368          | 0.8521   |
| 0.5328        | 36.0    | 63   | 0.6251          | 0.8592   |
| 0.5328        | 36.5714 | 64   | 0.6184          | 0.8732   |
| 0.5328        | 37.7143 | 66   | 0.6067          | 0.8732   |
| 0.5328        | 38.8571 | 68   | 0.6182          | 0.8662   |
| 0.5272        | 40.0    | 70   | 0.6398          | 0.8451   |
| 0.5272        | 40.5714 | 71   | 0.6440          | 0.8310   |
| 0.5272        | 41.7143 | 73   | 0.6318          | 0.8451   |
| 0.5272        | 42.8571 | 75   | 0.6111          | 0.8732   |
| 0.5272        | 44.0    | 77   | 0.6061          | 0.8732   |
| 0.5272        | 44.5714 | 78   | 0.6116          | 0.8732   |
| 0.5255        | 45.7143 | 80   | 0.6320          | 0.8451   |
| 0.5255        | 46.8571 | 82   | 0.6394          | 0.8310   |
| 0.5255        | 48.0    | 84   | 0.6379          | 0.8310   |
| 0.5255        | 48.5714 | 85   | 0.6363          | 0.8310   |
| 0.5255        | 49.7143 | 87   | 0.6282          | 0.8521   |
| 0.5255        | 50.8571 | 89   | 0.6214          | 0.8592   |
| 0.52          | 52.0    | 91   | 0.6195          | 0.8592   |
| 0.52          | 52.5714 | 92   | 0.6170          | 0.8662   |
| 0.52          | 53.7143 | 94   | 0.6169          | 0.8592   |
| 0.52          | 54.8571 | 96   | 0.6174          | 0.8592   |
| 0.52          | 56.0    | 98   | 0.6187          | 0.8592   |
| 0.52          | 56.5714 | 99   | 0.6193          | 0.8592   |
| 0.504         | 57.1429 | 100  | 0.6194          | 0.8592   |


### Framework versions

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

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