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# vit-msn-small-lateral_flow_ivalidation_green_channel

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: 0.5924
- Accuracy: 0.7454

## 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-07
- 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: reduce_lr_on_plateau
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9312        | 0.9231  | 6    | 0.9478          | 0.2788   |
| 0.7936        | 2.0     | 13   | 0.8935          | 0.2491   |
| 0.718         | 2.9231  | 19   | 0.8518          | 0.2249   |
| 0.6787        | 4.0     | 26   | 0.8097          | 0.2119   |
| 0.6538        | 4.9231  | 32   | 0.7776          | 0.2770   |
| 0.6265        | 6.0     | 39   | 0.7428          | 0.3662   |
| 0.5812        | 6.9231  | 45   | 0.7191          | 0.4424   |
| 0.6138        | 8.0     | 52   | 0.6996          | 0.4963   |
| 0.57          | 8.9231  | 58   | 0.6900          | 0.5409   |
| 0.5595        | 10.0    | 65   | 0.6804          | 0.5762   |
| 0.5288        | 10.9231 | 71   | 0.6727          | 0.6004   |
| 0.5094        | 12.0    | 78   | 0.6613          | 0.6320   |
| 0.5073        | 12.9231 | 84   | 0.6490          | 0.6636   |
| 0.4504        | 14.0    | 91   | 0.6386          | 0.6970   |
| 0.4868        | 14.9231 | 97   | 0.6305          | 0.7156   |
| 0.4799        | 16.0    | 104  | 0.6264          | 0.7175   |
| 0.4861        | 16.9231 | 110  | 0.6235          | 0.7175   |
| 0.4975        | 18.0    | 117  | 0.6163          | 0.7286   |
| 0.4712        | 18.9231 | 123  | 0.6127          | 0.7398   |
| 0.468         | 20.0    | 130  | 0.6107          | 0.7416   |
| 0.4562        | 20.9231 | 136  | 0.6070          | 0.7454   |
| 0.5195        | 22.0    | 143  | 0.6056          | 0.7454   |
| 0.4385        | 22.9231 | 149  | 0.6033          | 0.7416   |
| 0.4211        | 24.0    | 156  | 0.6050          | 0.7342   |
| 0.4364        | 24.9231 | 162  | 0.6023          | 0.7361   |
| 0.4327        | 26.0    | 169  | 0.5980          | 0.7416   |
| 0.4757        | 26.9231 | 175  | 0.6000          | 0.7361   |
| 0.4287        | 28.0    | 182  | 0.5924          | 0.7454   |
| 0.4313        | 28.9231 | 188  | 0.5970          | 0.7361   |
| 0.4483        | 30.0    | 195  | 0.5962          | 0.7398   |
| 0.3956        | 30.9231 | 201  | 0.5976          | 0.7305   |
| 0.41          | 32.0    | 208  | 0.6060          | 0.7212   |
| 0.4371        | 32.9231 | 214  | 0.6050          | 0.7193   |
| 0.4169        | 34.0    | 221  | 0.6045          | 0.7212   |
| 0.3882        | 34.9231 | 227  | 0.6020          | 0.7230   |
| 0.5097        | 36.0    | 234  | 0.6011          | 0.7286   |
| 0.476         | 36.9231 | 240  | 0.6027          | 0.7268   |
| 0.387         | 38.0    | 247  | 0.6012          | 0.7249   |
| 0.4744        | 38.9231 | 253  | 0.6017          | 0.7230   |
| 0.4712        | 40.0    | 260  | 0.6025          | 0.7230   |
| 0.4242        | 40.9231 | 266  | 0.6022          | 0.7230   |
| 0.4087        | 42.0    | 273  | 0.6021          | 0.7230   |
| 0.4009        | 42.9231 | 279  | 0.6026          | 0.7230   |
| 0.4219        | 44.0    | 286  | 0.6026          | 0.7230   |
| 0.4208        | 44.9231 | 292  | 0.6024          | 0.7230   |
| 0.3644        | 46.0    | 299  | 0.6013          | 0.7230   |
| 0.4458        | 46.9231 | 305  | 0.5997          | 0.7286   |
| 0.425         | 48.0    | 312  | 0.5991          | 0.7286   |
| 0.3982        | 48.9231 | 318  | 0.5995          | 0.7286   |
| 0.4167        | 50.0    | 325  | 0.5992          | 0.7286   |
| 0.4112        | 50.9231 | 331  | 0.5992          | 0.7286   |
| 0.4073        | 52.0    | 338  | 0.5992          | 0.7286   |
| 0.4413        | 52.9231 | 344  | 0.5991          | 0.7286   |
| 0.4326        | 54.0    | 351  | 0.5991          | 0.7286   |
| 0.4206        | 54.9231 | 357  | 0.5992          | 0.7286   |
| 0.3776        | 56.0    | 364  | 0.5993          | 0.7286   |
| 0.3792        | 56.9231 | 370  | 0.5994          | 0.7286   |
| 0.4075        | 58.0    | 377  | 0.5995          | 0.7286   |
| 0.4412        | 58.9231 | 383  | 0.5995          | 0.7286   |
| 0.4137        | 60.0    | 390  | 0.5995          | 0.7286   |
| 0.424         | 60.9231 | 396  | 0.5995          | 0.7286   |
| 0.3988        | 62.0    | 403  | 0.5997          | 0.7286   |
| 0.4167        | 62.9231 | 409  | 0.5996          | 0.7286   |
| 0.41          | 64.0    | 416  | 0.5997          | 0.7286   |
| 0.4235        | 64.9231 | 422  | 0.5997          | 0.7286   |
| 0.4544        | 66.0    | 429  | 0.5998          | 0.7286   |
| 0.4495        | 66.9231 | 435  | 0.5997          | 0.7286   |
| 0.424         | 68.0    | 442  | 0.5997          | 0.7286   |
| 0.4053        | 68.9231 | 448  | 0.5997          | 0.7286   |
| 0.426         | 70.0    | 455  | 0.5999          | 0.7286   |
| 0.3865        | 70.9231 | 461  | 0.6000          | 0.7286   |
| 0.3732        | 72.0    | 468  | 0.6001          | 0.7286   |
| 0.4289        | 72.9231 | 474  | 0.6002          | 0.7286   |
| 0.4524        | 74.0    | 481  | 0.6002          | 0.7286   |
| 0.4081        | 74.9231 | 487  | 0.6002          | 0.7286   |
| 0.384         | 76.0    | 494  | 0.6001          | 0.7286   |
| 0.4177        | 76.9231 | 500  | 0.6000          | 0.7286   |
| 0.3777        | 78.0    | 507  | 0.6000          | 0.7286   |
| 0.4226        | 78.9231 | 513  | 0.6000          | 0.7286   |
| 0.419         | 80.0    | 520  | 0.6000          | 0.7286   |
| 0.3956        | 80.9231 | 526  | 0.6000          | 0.7286   |
| 0.3669        | 82.0    | 533  | 0.6000          | 0.7286   |
| 0.3902        | 82.9231 | 539  | 0.6000          | 0.7286   |
| 0.4193        | 84.0    | 546  | 0.6001          | 0.7286   |
| 0.4115        | 84.9231 | 552  | 0.6001          | 0.7286   |
| 0.3923        | 86.0    | 559  | 0.6001          | 0.7286   |
| 0.4011        | 86.9231 | 565  | 0.6001          | 0.7286   |
| 0.4765        | 88.0    | 572  | 0.6000          | 0.7286   |
| 0.4034        | 88.9231 | 578  | 0.5999          | 0.7286   |
| 0.3867        | 90.0    | 585  | 0.5998          | 0.7286   |
| 0.4201        | 90.9231 | 591  | 0.5998          | 0.7286   |
| 0.4346        | 92.0    | 598  | 0.5998          | 0.7286   |
| 0.4171        | 92.3077 | 600  | 0.5998          | 0.7286   |


### Framework versions

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

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