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# vit-msn-small-lateral_flow_ivalidation_train_test_1

This model is a fine-tuned version of [Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_1](https://huggingface.co/Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_1) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3874
- Accuracy: 0.9084

## 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-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 100
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.3744        | 0.7692  | 1    | 0.3874          | 0.9084   |
| 0.3574        | 1.5385  | 2    | 0.3874          | 0.9084   |
| 0.3627        | 2.3077  | 3    | 0.3875          | 0.9084   |
| 0.3367        | 3.8462  | 5    | 0.3875          | 0.9084   |
| 0.3752        | 4.6154  | 6    | 0.3875          | 0.9084   |
| 0.3696        | 5.3846  | 7    | 0.3876          | 0.9084   |
| 0.3563        | 6.9231  | 9    | 0.3877          | 0.9084   |
| 0.3244        | 7.6923  | 10   | 0.3877          | 0.9084   |
| 0.3727        | 8.4615  | 11   | 0.3878          | 0.9084   |
| 0.3395        | 10.0    | 13   | 0.3879          | 0.9084   |
| 0.35          | 10.7692 | 14   | 0.3880          | 0.9084   |
| 0.3527        | 11.5385 | 15   | 0.3881          | 0.9084   |
| 0.3539        | 12.3077 | 16   | 0.3881          | 0.9084   |
| 0.348         | 13.8462 | 18   | 0.3883          | 0.9084   |
| 0.3584        | 14.6154 | 19   | 0.3884          | 0.9084   |
| 0.3491        | 15.3846 | 20   | 0.3884          | 0.9084   |
| 0.3603        | 16.9231 | 22   | 0.3886          | 0.9084   |
| 0.382         | 17.6923 | 23   | 0.3887          | 0.9084   |
| 0.3567        | 18.4615 | 24   | 0.3888          | 0.9084   |
| 0.3397        | 20.0    | 26   | 0.3889          | 0.9084   |
| 0.3682        | 20.7692 | 27   | 0.3890          | 0.9084   |
| 0.3336        | 21.5385 | 28   | 0.3891          | 0.9084   |
| 0.3399        | 22.3077 | 29   | 0.3892          | 0.9084   |
| 0.3656        | 23.8462 | 31   | 0.3893          | 0.9084   |
| 0.348         | 24.6154 | 32   | 0.3894          | 0.9084   |
| 0.3466        | 25.3846 | 33   | 0.3895          | 0.9084   |
| 0.3614        | 26.9231 | 35   | 0.3897          | 0.9084   |
| 0.3522        | 27.6923 | 36   | 0.3898          | 0.9084   |
| 0.3506        | 28.4615 | 37   | 0.3898          | 0.9084   |
| 0.3623        | 30.0    | 39   | 0.3900          | 0.9084   |
| 0.3562        | 30.7692 | 40   | 0.3901          | 0.9084   |
| 0.3515        | 31.5385 | 41   | 0.3902          | 0.9084   |
| 0.3744        | 32.3077 | 42   | 0.3903          | 0.9084   |
| 0.3382        | 33.8462 | 44   | 0.3904          | 0.9084   |
| 0.3467        | 34.6154 | 45   | 0.3905          | 0.9084   |
| 0.3713        | 35.3846 | 46   | 0.3906          | 0.9084   |
| 0.3653        | 36.9231 | 48   | 0.3908          | 0.9084   |
| 0.3359        | 37.6923 | 49   | 0.3909          | 0.9084   |
| 0.3745        | 38.4615 | 50   | 0.3910          | 0.9084   |
| 0.3594        | 40.0    | 52   | 0.3911          | 0.9084   |
| 0.3567        | 40.7692 | 53   | 0.3912          | 0.9084   |
| 0.3332        | 41.5385 | 54   | 0.3913          | 0.9084   |
| 0.3424        | 42.3077 | 55   | 0.3914          | 0.9084   |
| 0.3485        | 43.8462 | 57   | 0.3915          | 0.9084   |
| 0.3795        | 44.6154 | 58   | 0.3916          | 0.9084   |
| 0.321         | 45.3846 | 59   | 0.3917          | 0.9084   |
| 0.3314        | 46.9231 | 61   | 0.3918          | 0.9084   |
| 0.3582        | 47.6923 | 62   | 0.3919          | 0.9084   |
| 0.3355        | 48.4615 | 63   | 0.3920          | 0.9084   |
| 0.3688        | 50.0    | 65   | 0.3922          | 0.9084   |
| 0.3414        | 50.7692 | 66   | 0.3923          | 0.9084   |
| 0.3292        | 51.5385 | 67   | 0.3923          | 0.9084   |
| 0.3622        | 52.3077 | 68   | 0.3924          | 0.9048   |
| 0.3446        | 53.8462 | 70   | 0.3926          | 0.9048   |
| 0.3678        | 54.6154 | 71   | 0.3927          | 0.9011   |
| 0.3632        | 55.3846 | 72   | 0.3927          | 0.9011   |
| 0.3459        | 56.9231 | 74   | 0.3929          | 0.9011   |
| 0.3435        | 57.6923 | 75   | 0.3929          | 0.9011   |
| 0.3461        | 58.4615 | 76   | 0.3930          | 0.9011   |
| 0.3423        | 60.0    | 78   | 0.3931          | 0.9011   |
| 0.3632        | 60.7692 | 79   | 0.3932          | 0.8974   |
| 0.3536        | 61.5385 | 80   | 0.3933          | 0.8974   |
| 0.3556        | 62.3077 | 81   | 0.3934          | 0.8974   |
| 0.3391        | 63.8462 | 83   | 0.3935          | 0.8974   |
| 0.3482        | 64.6154 | 84   | 0.3936          | 0.8974   |
| 0.3339        | 65.3846 | 85   | 0.3937          | 0.8974   |
| 0.3438        | 66.9231 | 87   | 0.3938          | 0.8974   |
| 0.3198        | 67.6923 | 88   | 0.3939          | 0.8974   |
| 0.3399        | 68.4615 | 89   | 0.3939          | 0.8974   |
| 0.3388        | 70.0    | 91   | 0.3941          | 0.8974   |
| 0.3519        | 70.7692 | 92   | 0.3942          | 0.8974   |
| 0.3445        | 71.5385 | 93   | 0.3942          | 0.8974   |
| 0.3423        | 72.3077 | 94   | 0.3943          | 0.8974   |
| 0.3392        | 73.8462 | 96   | 0.3945          | 0.8974   |
| 0.3576        | 74.6154 | 97   | 0.3945          | 0.8974   |
| 0.3526        | 75.3846 | 98   | 0.3946          | 0.8974   |
| 0.3714        | 76.9231 | 100  | 0.3947          | 0.8974   |


### Framework versions

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

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