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# vit-msn-small-lateral_flow_ivalidation_green

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: 0.2917
- Accuracy: 0.9602

## 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: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 6    | 0.5034          | 0.9239   |
| 0.704         | 2.0     | 13   | 0.4524          | 0.9410   |
| 0.704         | 2.9231  | 19   | 0.2633          | 0.9594   |
| 0.505         | 4.0     | 26   | 0.4748          | 0.8092   |
| 0.4456        | 4.9231  | 32   | 0.2917          | 0.9602   |
| 0.4456        | 6.0     | 39   | 0.2621          | 0.9222   |
| 0.3908        | 6.9231  | 45   | 0.4519          | 0.8191   |
| 0.3628        | 8.0     | 52   | 0.4093          | 0.8623   |
| 0.3628        | 8.9231  | 58   | 0.2705          | 0.9354   |
| 0.372         | 10.0    | 65   | 0.4137          | 0.8546   |
| 0.36          | 10.9231 | 71   | 0.3493          | 0.8815   |
| 0.36          | 12.0    | 78   | 0.2190          | 0.9457   |
| 0.36          | 12.9231 | 84   | 0.3190          | 0.9033   |
| 0.3363        | 14.0    | 91   | 0.3380          | 0.8948   |
| 0.3363        | 14.9231 | 97   | 0.3342          | 0.8982   |
| 0.327         | 16.0    | 104  | 0.4212          | 0.8328   |
| 0.3257        | 16.9231 | 110  | 0.5167          | 0.7844   |
| 0.3257        | 18.0    | 117  | 0.5848          | 0.7275   |
| 0.3175        | 18.9231 | 123  | 0.4091          | 0.8336   |
| 0.3377        | 20.0    | 130  | 0.2838          | 0.9162   |
| 0.3377        | 20.9231 | 136  | 0.6106          | 0.7263   |
| 0.3129        | 22.0    | 143  | 0.6295          | 0.7164   |
| 0.3129        | 22.9231 | 149  | 0.7898          | 0.5932   |
| 0.3138        | 24.0    | 156  | 0.9408          | 0.4846   |
| 0.3106        | 24.9231 | 162  | 0.3485          | 0.8832   |
| 0.3106        | 26.0    | 169  | 0.5201          | 0.7866   |
| 0.3157        | 26.9231 | 175  | 0.7210          | 0.6672   |
| 0.2896        | 28.0    | 182  | 0.7981          | 0.6330   |
| 0.2896        | 28.9231 | 188  | 0.7667          | 0.6429   |
| 0.2867        | 30.0    | 195  | 0.7687          | 0.6544   |
| 0.2786        | 30.9231 | 201  | 1.1714          | 0.5210   |
| 0.2786        | 32.0    | 208  | 1.1744          | 0.4273   |
| 0.2823        | 32.9231 | 214  | 0.9260          | 0.5445   |
| 0.2864        | 34.0    | 221  | 0.7140          | 0.6920   |
| 0.2864        | 34.9231 | 227  | 0.6098          | 0.7331   |
| 0.2707        | 36.0    | 234  | 0.6993          | 0.6784   |
| 0.2921        | 36.9231 | 240  | 0.8719          | 0.6176   |
| 0.2921        | 38.0    | 247  | 0.8337          | 0.6061   |
| 0.2849        | 38.9231 | 253  | 0.4396          | 0.8255   |
| 0.2657        | 40.0    | 260  | 1.0982          | 0.5017   |
| 0.2657        | 40.9231 | 266  | 1.0934          | 0.5175   |
| 0.2659        | 42.0    | 273  | 0.8629          | 0.6369   |
| 0.2659        | 42.9231 | 279  | 1.4602          | 0.4140   |
| 0.2645        | 44.0    | 286  | 1.9095          | 0.3422   |
| 0.2424        | 44.9231 | 292  | 1.2180          | 0.4397   |
| 0.2424        | 46.0    | 299  | 0.7686          | 0.6424   |
| 0.2495        | 46.9231 | 305  | 0.9899          | 0.5796   |
| 0.2454        | 48.0    | 312  | 1.0291          | 0.5535   |
| 0.2454        | 48.9231 | 318  | 0.7534          | 0.6822   |
| 0.2473        | 50.0    | 325  | 0.6591          | 0.7092   |
| 0.2716        | 50.9231 | 331  | 0.5840          | 0.7455   |
| 0.2716        | 52.0    | 338  | 1.2430          | 0.4765   |
| 0.234         | 52.9231 | 344  | 1.2993          | 0.5145   |
| 0.2482        | 54.0    | 351  | 0.6042          | 0.7173   |
| 0.2482        | 54.9231 | 357  | 0.8892          | 0.6027   |
| 0.2339        | 56.0    | 364  | 1.8546          | 0.3161   |
| 0.2461        | 56.9231 | 370  | 1.0859          | 0.5359   |
| 0.2461        | 58.0    | 377  | 0.8690          | 0.6176   |
| 0.2395        | 58.9231 | 383  | 0.7557          | 0.6694   |
| 0.2159        | 60.0    | 390  | 1.0534          | 0.5701   |
| 0.2159        | 60.9231 | 396  | 0.9856          | 0.5813   |
| 0.2309        | 62.0    | 403  | 1.0000          | 0.5500   |
| 0.2309        | 62.9231 | 409  | 1.1940          | 0.5180   |
| 0.2117        | 64.0    | 416  | 1.1581          | 0.5154   |
| 0.2307        | 64.9231 | 422  | 0.9987          | 0.5338   |
| 0.2307        | 66.0    | 429  | 1.0850          | 0.5415   |
| 0.2068        | 66.9231 | 435  | 0.9428          | 0.6014   |
| 0.2126        | 68.0    | 442  | 1.2380          | 0.5115   |
| 0.2126        | 68.9231 | 448  | 0.9993          | 0.5860   |
| 0.2176        | 70.0    | 455  | 1.1910          | 0.5021   |
| 0.2096        | 70.9231 | 461  | 1.2468          | 0.5120   |
| 0.2096        | 72.0    | 468  | 0.7588          | 0.6920   |
| 0.2092        | 72.9231 | 474  | 0.9003          | 0.6309   |
| 0.1968        | 74.0    | 481  | 1.1697          | 0.5646   |
| 0.1968        | 74.9231 | 487  | 0.8789          | 0.6446   |
| 0.2027        | 76.0    | 494  | 1.1352          | 0.5599   |
| 0.1965        | 76.9231 | 500  | 1.0836          | 0.5599   |
| 0.1965        | 78.0    | 507  | 1.0188          | 0.5902   |
| 0.2267        | 78.9231 | 513  | 1.0287          | 0.5975   |
| 0.1967        | 80.0    | 520  | 0.8465          | 0.6544   |
| 0.1967        | 80.9231 | 526  | 1.1881          | 0.5470   |
| 0.1842        | 82.0    | 533  | 1.2352          | 0.5368   |
| 0.1842        | 82.9231 | 539  | 1.1064          | 0.5701   |
| 0.1952        | 84.0    | 546  | 0.8088          | 0.6608   |
| 0.1873        | 84.9231 | 552  | 0.9342          | 0.6086   |
| 0.1873        | 86.0    | 559  | 0.9807          | 0.6056   |
| 0.185         | 86.9231 | 565  | 1.0165          | 0.5898   |
| 0.1993        | 88.0    | 572  | 1.1511          | 0.5475   |
| 0.1993        | 88.9231 | 578  | 1.1766          | 0.5406   |
| 0.1707        | 90.0    | 585  | 1.1201          | 0.5663   |
| 0.1852        | 90.9231 | 591  | 1.1162          | 0.5701   |
| 0.1852        | 92.0    | 598  | 1.1273          | 0.5680   |
| 0.1904        | 92.3077 | 600  | 1.1280          | 0.5680   |


### Framework versions

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

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