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# vit-msn-small-corect_dataset_lateral_flow_ivalidation

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: 0.2930
- Accuracy: 0.9048

## 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: 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: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 3    | 0.6350          | 0.6337   |
| No log        | 1.8462  | 6    | 0.5047          | 0.8022   |
| No log        | 2.7692  | 9    | 0.3701          | 0.8791   |
| 0.5485        | 4.0     | 13   | 0.5379          | 0.7436   |
| 0.5485        | 4.9231  | 16   | 0.2748          | 0.8938   |
| 0.5485        | 5.8462  | 19   | 0.3004          | 0.8974   |
| 0.3335        | 6.7692  | 22   | 0.3492          | 0.8681   |
| 0.3335        | 8.0     | 26   | 0.2497          | 0.8974   |
| 0.3335        | 8.9231  | 29   | 0.4304          | 0.8315   |
| 0.3087        | 9.8462  | 32   | 0.3479          | 0.8791   |
| 0.3087        | 10.7692 | 35   | 0.3796          | 0.8645   |
| 0.3087        | 12.0    | 39   | 0.4152          | 0.8352   |
| 0.2614        | 12.9231 | 42   | 0.3199          | 0.9011   |
| 0.2614        | 13.8462 | 45   | 0.3434          | 0.8718   |
| 0.2614        | 14.7692 | 48   | 0.4001          | 0.8462   |
| 0.2471        | 16.0    | 52   | 0.3220          | 0.8901   |
| 0.2471        | 16.9231 | 55   | 0.3540          | 0.8718   |
| 0.2471        | 17.8462 | 58   | 0.4019          | 0.8535   |
| 0.2817        | 18.7692 | 61   | 0.3152          | 0.8974   |
| 0.2817        | 20.0    | 65   | 0.3978          | 0.8571   |
| 0.2817        | 20.9231 | 68   | 0.4289          | 0.8388   |
| 0.2353        | 21.8462 | 71   | 0.3146          | 0.8974   |
| 0.2353        | 22.7692 | 74   | 0.3206          | 0.8864   |
| 0.2353        | 24.0    | 78   | 0.3715          | 0.8828   |
| 0.2339        | 24.9231 | 81   | 0.3446          | 0.8938   |
| 0.2339        | 25.8462 | 84   | 0.2930          | 0.9048   |
| 0.2339        | 26.7692 | 87   | 0.4349          | 0.8205   |
| 0.2301        | 28.0    | 91   | 0.3630          | 0.8681   |
| 0.2301        | 28.9231 | 94   | 0.3669          | 0.8645   |
| 0.2301        | 29.8462 | 97   | 0.5037          | 0.7912   |
| 0.2115        | 30.7692 | 100  | 0.3449          | 0.8828   |
| 0.2115        | 32.0    | 104  | 0.3280          | 0.9011   |
| 0.2115        | 32.9231 | 107  | 0.4031          | 0.8425   |
| 0.2033        | 33.8462 | 110  | 0.3612          | 0.8535   |
| 0.2033        | 34.7692 | 113  | 0.3163          | 0.8901   |
| 0.2033        | 36.0    | 117  | 0.3234          | 0.8864   |
| 0.1807        | 36.9231 | 120  | 0.3307          | 0.8791   |


### Framework versions

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

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