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# vit-msn-small-lateral_flow_ivalidation_train_test_6

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: 0.4371
- Accuracy: 0.8791

## 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: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.6672        | 0.9231  | 6    | 0.6980          | 0.4212   |
| 0.6617        | 2.0     | 13   | 0.6965          | 0.4249   |
| 0.6699        | 2.9231  | 19   | 0.6944          | 0.4396   |
| 0.662         | 4.0     | 26   | 0.6910          | 0.4396   |
| 0.6548        | 4.9231  | 32   | 0.6873          | 0.4579   |
| 0.6541        | 6.0     | 39   | 0.6825          | 0.4835   |
| 0.6222        | 6.9231  | 45   | 0.6777          | 0.5311   |
| 0.6555        | 8.0     | 52   | 0.6719          | 0.5421   |
| 0.6226        | 8.9231  | 58   | 0.6665          | 0.5861   |
| 0.5989        | 10.0    | 65   | 0.6603          | 0.6154   |
| 0.5754        | 10.9231 | 71   | 0.6555          | 0.6264   |
| 0.6251        | 12.0    | 78   | 0.6493          | 0.6484   |
| 0.5796        | 12.9231 | 84   | 0.6446          | 0.6667   |
| 0.5763        | 14.0    | 91   | 0.6390          | 0.6667   |
| 0.5952        | 14.9231 | 97   | 0.6333          | 0.6850   |
| 0.5675        | 16.0    | 104  | 0.6269          | 0.7033   |
| 0.5453        | 16.9231 | 110  | 0.6211          | 0.7106   |
| 0.5199        | 18.0    | 117  | 0.6150          | 0.7143   |
| 0.541         | 18.9231 | 123  | 0.6090          | 0.7216   |
| 0.5273        | 20.0    | 130  | 0.6007          | 0.7289   |
| 0.495         | 20.9231 | 136  | 0.5934          | 0.7289   |
| 0.4855        | 22.0    | 143  | 0.5855          | 0.7473   |
| 0.4763        | 22.9231 | 149  | 0.5787          | 0.7363   |
| 0.4287        | 24.0    | 156  | 0.5693          | 0.7509   |
| 0.445         | 24.9231 | 162  | 0.5619          | 0.7692   |
| 0.4343        | 26.0    | 169  | 0.5540          | 0.7802   |
| 0.3748        | 26.9231 | 175  | 0.5467          | 0.7875   |
| 0.4041        | 28.0    | 182  | 0.5421          | 0.8022   |
| 0.3543        | 28.9231 | 188  | 0.5291          | 0.8205   |
| 0.3972        | 30.0    | 195  | 0.5134          | 0.8278   |
| 0.3716        | 30.9231 | 201  | 0.5150          | 0.8242   |
| 0.3871        | 32.0    | 208  | 0.5100          | 0.8315   |
| 0.3729        | 32.9231 | 214  | 0.4986          | 0.8352   |
| 0.3286        | 34.0    | 221  | 0.4946          | 0.8462   |
| 0.4261        | 34.9231 | 227  | 0.4957          | 0.8388   |
| 0.4014        | 36.0    | 234  | 0.4850          | 0.8535   |
| 0.3514        | 36.9231 | 240  | 0.4807          | 0.8535   |
| 0.3883        | 38.0    | 247  | 0.4767          | 0.8535   |
| 0.3219        | 38.9231 | 253  | 0.4763          | 0.8535   |
| 0.4351        | 40.0    | 260  | 0.4738          | 0.8571   |
| 0.3068        | 40.9231 | 266  | 0.4688          | 0.8645   |
| 0.3356        | 42.0    | 273  | 0.4585          | 0.8645   |
| 0.345         | 42.9231 | 279  | 0.4541          | 0.8681   |
| 0.3254        | 44.0    | 286  | 0.4584          | 0.8645   |
| 0.3164        | 44.9231 | 292  | 0.4592          | 0.8571   |
| 0.3657        | 46.0    | 299  | 0.4534          | 0.8608   |
| 0.2655        | 46.9231 | 305  | 0.4502          | 0.8645   |
| 0.2981        | 48.0    | 312  | 0.4452          | 0.8645   |
| 0.3508        | 48.9231 | 318  | 0.4371          | 0.8791   |
| 0.3419        | 50.0    | 325  | 0.4394          | 0.8755   |
| 0.2668        | 50.9231 | 331  | 0.4430          | 0.8755   |
| 0.2972        | 52.0    | 338  | 0.4395          | 0.8718   |
| 0.3514        | 52.9231 | 344  | 0.4371          | 0.8755   |
| 0.3012        | 54.0    | 351  | 0.4330          | 0.8791   |
| 0.2725        | 54.9231 | 357  | 0.4298          | 0.8791   |
| 0.2547        | 56.0    | 364  | 0.4289          | 0.8718   |
| 0.2896        | 56.9231 | 370  | 0.4282          | 0.8718   |
| 0.3469        | 58.0    | 377  | 0.4273          | 0.8718   |
| 0.3528        | 58.9231 | 383  | 0.4269          | 0.8718   |
| 0.2552        | 60.0    | 390  | 0.4324          | 0.8681   |
| 0.239         | 60.9231 | 396  | 0.4319          | 0.8645   |
| 0.3321        | 62.0    | 403  | 0.4270          | 0.8718   |
| 0.3115        | 62.9231 | 409  | 0.4184          | 0.8718   |
| 0.306         | 64.0    | 416  | 0.4169          | 0.8718   |
| 0.3086        | 64.9231 | 422  | 0.4176          | 0.8718   |
| 0.4256        | 66.0    | 429  | 0.4196          | 0.8718   |
| 0.2798        | 66.9231 | 435  | 0.4219          | 0.8718   |
| 0.3016        | 68.0    | 442  | 0.4224          | 0.8718   |
| 0.2791        | 68.9231 | 448  | 0.4207          | 0.8718   |
| 0.2651        | 70.0    | 455  | 0.4189          | 0.8718   |
| 0.2466        | 70.9231 | 461  | 0.4178          | 0.8718   |
| 0.1913        | 72.0    | 468  | 0.4177          | 0.8718   |
| 0.2719        | 72.9231 | 474  | 0.4164          | 0.8718   |
| 0.3364        | 74.0    | 481  | 0.4166          | 0.8718   |
| 0.283         | 74.9231 | 487  | 0.4179          | 0.8755   |
| 0.2891        | 76.0    | 494  | 0.4174          | 0.8755   |
| 0.2625        | 76.9231 | 500  | 0.4180          | 0.8755   |
| 0.2843        | 78.0    | 507  | 0.4184          | 0.8718   |
| 0.375         | 78.9231 | 513  | 0.4167          | 0.8755   |
| 0.3107        | 80.0    | 520  | 0.4150          | 0.8755   |
| 0.3742        | 80.9231 | 526  | 0.4145          | 0.8718   |
| 0.2574        | 82.0    | 533  | 0.4145          | 0.8755   |
| 0.329         | 82.9231 | 539  | 0.4149          | 0.8755   |
| 0.2727        | 84.0    | 546  | 0.4145          | 0.8755   |
| 0.2977        | 84.9231 | 552  | 0.4149          | 0.8755   |
| 0.2611        | 86.0    | 559  | 0.4160          | 0.8718   |
| 0.2542        | 86.9231 | 565  | 0.4170          | 0.8718   |
| 0.2665        | 88.0    | 572  | 0.4171          | 0.8718   |
| 0.2654        | 88.9231 | 578  | 0.4170          | 0.8718   |
| 0.3059        | 90.0    | 585  | 0.4172          | 0.8718   |
| 0.2377        | 90.9231 | 591  | 0.4173          | 0.8718   |
| 0.2896        | 92.0    | 598  | 0.4172          | 0.8718   |
| 0.3133        | 92.3077 | 600  | 0.4172          | 0.8718   |


### Framework versions

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

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