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# vit-msn-small-lateral_flow_ivalidation_train_test

This model is a fine-tuned version of [Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test](https://huggingface.co/Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3718
- Accuracy: 0.9121

## 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: 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: 0.05
- num_epochs: 100
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.4023        | 0.9231  | 3    | 0.4126          | 0.8938   |
| 0.4055        | 1.8462  | 6    | 0.4096          | 0.8974   |
| 0.4112        | 2.7692  | 9    | 0.4275          | 0.8974   |
| 0.4246        | 4.0     | 13   | 0.3958          | 0.9048   |
| 0.376         | 4.9231  | 16   | 0.3980          | 0.9121   |
| 0.4226        | 5.8462  | 19   | 0.4029          | 0.9084   |
| 0.3751        | 6.7692  | 22   | 0.3749          | 0.9048   |
| 0.4135        | 8.0     | 26   | 0.3757          | 0.9121   |
| 0.3673        | 8.9231  | 29   | 0.4174          | 0.8901   |
| 0.3749        | 9.8462  | 32   | 0.4077          | 0.9048   |
| 0.4119        | 10.7692 | 35   | 0.4181          | 0.8901   |
| 0.3946        | 12.0    | 39   | 0.4189          | 0.8901   |
| 0.3335        | 12.9231 | 42   | 0.4029          | 0.9084   |
| 0.3717        | 13.8462 | 45   | 0.3963          | 0.9011   |
| 0.3493        | 14.7692 | 48   | 0.3797          | 0.9158   |
| 0.3686        | 16.0    | 52   | 0.3761          | 0.9121   |
| 0.3999        | 16.9231 | 55   | 0.3774          | 0.9158   |
| 0.3221        | 17.8462 | 58   | 0.3757          | 0.9158   |
| 0.3902        | 18.7692 | 61   | 0.3774          | 0.9121   |
| 0.3649        | 20.0    | 65   | 0.3962          | 0.9011   |
| 0.3553        | 20.9231 | 68   | 0.3718          | 0.9121   |
| 0.3761        | 21.8462 | 71   | 0.3934          | 0.9121   |
| 0.3422        | 22.7692 | 74   | 0.4271          | 0.8828   |
| 0.3247        | 24.0    | 78   | 0.3727          | 0.9194   |
| 0.3417        | 24.9231 | 81   | 0.3793          | 0.9121   |
| 0.3499        | 25.8462 | 84   | 0.4293          | 0.8791   |
| 0.3397        | 26.7692 | 87   | 0.4216          | 0.8901   |
| 0.346         | 28.0    | 91   | 0.4001          | 0.8901   |
| 0.3337        | 28.9231 | 94   | 0.4168          | 0.8864   |
| 0.3268        | 29.8462 | 97   | 0.4123          | 0.8938   |
| 0.3274        | 30.7692 | 100  | 0.4187          | 0.8828   |
| 0.3757        | 32.0    | 104  | 0.4026          | 0.8974   |
| 0.3727        | 32.9231 | 107  | 0.4021          | 0.8938   |
| 0.3431        | 33.8462 | 110  | 0.4024          | 0.9011   |
| 0.3626        | 34.7692 | 113  | 0.4200          | 0.8901   |
| 0.3381        | 36.0    | 117  | 0.4080          | 0.8938   |
| 0.3411        | 36.9231 | 120  | 0.4279          | 0.8791   |
| 0.3229        | 37.8462 | 123  | 0.4422          | 0.8718   |
| 0.3736        | 38.7692 | 126  | 0.4285          | 0.8791   |
| 0.4145        | 40.0    | 130  | 0.4402          | 0.8718   |
| 0.3456        | 40.9231 | 133  | 0.4226          | 0.8828   |
| 0.3567        | 41.8462 | 136  | 0.4113          | 0.8901   |
| 0.339         | 42.7692 | 139  | 0.4445          | 0.8645   |
| 0.3142        | 44.0    | 143  | 0.4204          | 0.8791   |
| 0.3461        | 44.9231 | 146  | 0.4006          | 0.8974   |
| 0.3583        | 45.8462 | 149  | 0.3991          | 0.9011   |
| 0.3651        | 46.7692 | 152  | 0.4293          | 0.8681   |
| 0.3098        | 48.0    | 156  | 0.4082          | 0.8901   |
| 0.375         | 48.9231 | 159  | 0.4095          | 0.8864   |
| 0.3435        | 49.8462 | 162  | 0.4529          | 0.8498   |
| 0.3452        | 50.7692 | 165  | 0.4440          | 0.8608   |
| 0.3316        | 52.0    | 169  | 0.4181          | 0.8791   |
| 0.3344        | 52.9231 | 172  | 0.4609          | 0.8535   |
| 0.3377        | 53.8462 | 175  | 0.4775          | 0.8278   |
| 0.3455        | 54.7692 | 178  | 0.4396          | 0.8681   |
| 0.3202        | 56.0    | 182  | 0.4384          | 0.8755   |
| 0.3119        | 56.9231 | 185  | 0.4573          | 0.8535   |
| 0.3633        | 57.8462 | 188  | 0.4469          | 0.8645   |
| 0.3025        | 58.7692 | 191  | 0.4437          | 0.8608   |
| 0.3094        | 60.0    | 195  | 0.4472          | 0.8571   |
| 0.3306        | 60.9231 | 198  | 0.4396          | 0.8681   |
| 0.3266        | 61.8462 | 201  | 0.4486          | 0.8681   |
| 0.3495        | 62.7692 | 204  | 0.4658          | 0.8352   |
| 0.3066        | 64.0    | 208  | 0.4754          | 0.8315   |
| 0.3384        | 64.9231 | 211  | 0.4518          | 0.8608   |
| 0.3151        | 65.8462 | 214  | 0.4614          | 0.8535   |
| 0.3233        | 66.7692 | 217  | 0.4638          | 0.8425   |
| 0.3416        | 68.0    | 221  | 0.4741          | 0.8315   |
| 0.3326        | 68.9231 | 224  | 0.4679          | 0.8425   |
| 0.331         | 69.8462 | 227  | 0.4754          | 0.8315   |
| 0.3595        | 70.7692 | 230  | 0.4603          | 0.8498   |
| 0.3107        | 72.0    | 234  | 0.4412          | 0.8571   |
| 0.3126        | 72.9231 | 237  | 0.4578          | 0.8571   |
| 0.3205        | 73.8462 | 240  | 0.4820          | 0.8242   |
| 0.3296        | 74.7692 | 243  | 0.5048          | 0.7985   |
| 0.3246        | 76.0    | 247  | 0.4792          | 0.8278   |
| 0.3065        | 76.9231 | 250  | 0.4842          | 0.8242   |
| 0.282         | 77.8462 | 253  | 0.5049          | 0.7912   |
| 0.3272        | 78.7692 | 256  | 0.5088          | 0.7875   |
| 0.325         | 80.0    | 260  | 0.4933          | 0.8132   |
| 0.3524        | 80.9231 | 263  | 0.4893          | 0.8132   |
| 0.3019        | 81.8462 | 266  | 0.4864          | 0.8132   |
| 0.3095        | 82.7692 | 269  | 0.4875          | 0.8132   |
| 0.3254        | 84.0    | 273  | 0.4910          | 0.8059   |
| 0.3158        | 84.9231 | 276  | 0.4918          | 0.8059   |
| 0.3114        | 85.8462 | 279  | 0.4936          | 0.8059   |
| 0.3348        | 86.7692 | 282  | 0.4996          | 0.7985   |
| 0.3078        | 88.0    | 286  | 0.5043          | 0.7949   |
| 0.3096        | 88.9231 | 289  | 0.5047          | 0.7949   |
| 0.2827        | 89.8462 | 292  | 0.5054          | 0.7949   |
| 0.3249        | 90.7692 | 295  | 0.5040          | 0.7949   |
| 0.3277        | 92.0    | 299  | 0.5031          | 0.7985   |
| 0.3522        | 92.3077 | 300  | 0.5030          | 0.7985   |


### Framework versions

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

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