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# vit-msn-small-ultralytics_yolo_cropped_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: 0.1774
- Accuracy: 0.9489

## 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    | 0.8678          | 0.4277   |
| No log        | 1.8462  | 6    | 0.6171          | 0.7      |
| No log        | 2.7692  | 9    | 0.4174          | 0.8723   |
| 0.6518        | 4.0     | 13   | 0.5366          | 0.7106   |
| 0.6518        | 4.9231  | 16   | 0.3255          | 0.8851   |
| 0.6518        | 5.8462  | 19   | 0.6159          | 0.6809   |
| 0.4119        | 6.7692  | 22   | 0.3017          | 0.9191   |
| 0.4119        | 8.0     | 26   | 0.5130          | 0.7128   |
| 0.4119        | 8.9231  | 29   | 0.2183          | 0.9255   |
| 0.3387        | 9.8462  | 32   | 0.2523          | 0.9149   |
| 0.3387        | 10.7692 | 35   | 0.1774          | 0.9489   |
| 0.3387        | 12.0    | 39   | 0.2376          | 0.9255   |
| 0.3055        | 12.9231 | 42   | 0.3930          | 0.8383   |
| 0.3055        | 13.8462 | 45   | 0.2308          | 0.9234   |
| 0.3055        | 14.7692 | 48   | 0.1587          | 0.9468   |
| 0.2909        | 16.0    | 52   | 0.6113          | 0.6830   |
| 0.2909        | 16.9231 | 55   | 0.2910          | 0.8915   |
| 0.2909        | 17.8462 | 58   | 0.3612          | 0.8447   |
| 0.2227        | 18.7692 | 61   | 0.3117          | 0.8787   |
| 0.2227        | 20.0    | 65   | 0.2684          | 0.9170   |
| 0.2227        | 20.9231 | 68   | 0.3767          | 0.8404   |
| 0.2129        | 21.8462 | 71   | 0.2527          | 0.9234   |
| 0.2129        | 22.7692 | 74   | 0.3270          | 0.8745   |
| 0.2129        | 24.0    | 78   | 0.4314          | 0.8064   |
| 0.213         | 24.9231 | 81   | 0.2874          | 0.9      |
| 0.213         | 25.8462 | 84   | 0.4797          | 0.7894   |
| 0.213         | 26.7692 | 87   | 0.4896          | 0.7851   |
| 0.1758        | 28.0    | 91   | 0.3144          | 0.8723   |
| 0.1758        | 28.9231 | 94   | 0.5881          | 0.7213   |
| 0.1758        | 29.8462 | 97   | 0.5599          | 0.7298   |
| 0.1766        | 30.7692 | 100  | 0.3413          | 0.8702   |
| 0.1766        | 32.0    | 104  | 0.3453          | 0.8638   |
| 0.1766        | 32.9231 | 107  | 0.3634          | 0.8596   |
| 0.1583        | 33.8462 | 110  | 0.3799          | 0.8468   |
| 0.1583        | 34.7692 | 113  | 0.3840          | 0.8468   |
| 0.1583        | 36.0    | 117  | 0.3890          | 0.8447   |
| 0.1969        | 36.9231 | 120  | 0.3950          | 0.8426   |


### Framework versions

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

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