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# vit-msn-small-lateral_flow_ivalidation_train_test_4

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: 0.3980
- Accuracy: 0.8938

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8038        | 1.0   | 13   | 0.8368          | 0.4029   |
| 0.6874        | 2.0   | 26   | 0.8356          | 0.4029   |
| 0.6487        | 3.0   | 39   | 0.8336          | 0.3810   |
| 0.773         | 4.0   | 52   | 0.8307          | 0.3700   |
| 0.7002        | 5.0   | 65   | 0.8270          | 0.3480   |
| 0.6991        | 6.0   | 78   | 0.8223          | 0.3407   |
| 0.6809        | 7.0   | 91   | 0.8164          | 0.3480   |
| 0.7359        | 8.0   | 104  | 0.8093          | 0.3516   |
| 0.771         | 9.0   | 117  | 0.8017          | 0.3443   |
| 0.6855        | 10.0  | 130  | 0.7934          | 0.3443   |
| 0.6674        | 11.0  | 143  | 0.7851          | 0.3480   |
| 0.6296        | 12.0  | 156  | 0.7746          | 0.3810   |
| 0.5597        | 13.0  | 169  | 0.7643          | 0.3956   |
| 0.5636        | 14.0  | 182  | 0.7519          | 0.4066   |
| 0.5718        | 15.0  | 195  | 0.7382          | 0.4432   |
| 0.5527        | 16.0  | 208  | 0.7256          | 0.4579   |
| 0.5646        | 17.0  | 221  | 0.7115          | 0.5055   |
| 0.4843        | 18.0  | 234  | 0.6966          | 0.5275   |
| 0.492         | 19.0  | 247  | 0.6805          | 0.5788   |
| 0.4865        | 20.0  | 260  | 0.6630          | 0.6117   |
| 0.4198        | 21.0  | 273  | 0.6448          | 0.6410   |
| 0.4203        | 22.0  | 286  | 0.6280          | 0.6740   |
| 0.4547        | 23.0  | 299  | 0.6083          | 0.6923   |
| 0.3916        | 24.0  | 312  | 0.5909          | 0.7143   |
| 0.4329        | 25.0  | 325  | 0.5768          | 0.7289   |
| 0.4645        | 26.0  | 338  | 0.5629          | 0.7399   |
| 0.3376        | 27.0  | 351  | 0.5536          | 0.7436   |
| 0.4417        | 28.0  | 364  | 0.5417          | 0.7729   |
| 0.3908        | 29.0  | 377  | 0.5262          | 0.7619   |
| 0.3715        | 30.0  | 390  | 0.5130          | 0.7729   |
| 0.438         | 31.0  | 403  | 0.5059          | 0.7912   |
| 0.2937        | 32.0  | 416  | 0.4937          | 0.8022   |
| 0.2944        | 33.0  | 429  | 0.4871          | 0.8022   |
| 0.3474        | 34.0  | 442  | 0.4820          | 0.8059   |
| 0.2302        | 35.0  | 455  | 0.4776          | 0.7949   |
| 0.3543        | 36.0  | 468  | 0.4690          | 0.8022   |
| 0.3325        | 37.0  | 481  | 0.4640          | 0.8059   |
| 0.4004        | 38.0  | 494  | 0.4584          | 0.8095   |
| 0.3031        | 39.0  | 507  | 0.4548          | 0.8132   |
| 0.4862        | 40.0  | 520  | 0.4520          | 0.8095   |
| 0.2609        | 41.0  | 533  | 0.4498          | 0.8278   |
| 0.1859        | 42.0  | 546  | 0.4450          | 0.8462   |
| 0.2712        | 43.0  | 559  | 0.4408          | 0.8462   |
| 0.221         | 44.0  | 572  | 0.4387          | 0.8425   |
| 0.2328        | 45.0  | 585  | 0.4371          | 0.8498   |
| 0.3004        | 46.0  | 598  | 0.4339          | 0.8425   |
| 0.2036        | 47.0  | 611  | 0.4318          | 0.8462   |
| 0.1925        | 48.0  | 624  | 0.4299          | 0.8498   |
| 0.4543        | 49.0  | 637  | 0.4266          | 0.8498   |
| 0.4056        | 50.0  | 650  | 0.4251          | 0.8462   |
| 0.2326        | 51.0  | 663  | 0.4247          | 0.8498   |
| 0.327         | 52.0  | 676  | 0.4224          | 0.8571   |
| 0.2385        | 53.0  | 689  | 0.4193          | 0.8571   |
| 0.2876        | 54.0  | 702  | 0.4183          | 0.8571   |
| 0.2257        | 55.0  | 715  | 0.4162          | 0.8718   |
| 0.252         | 56.0  | 728  | 0.4150          | 0.8755   |
| 0.4299        | 57.0  | 741  | 0.4129          | 0.8645   |
| 0.3146        | 58.0  | 754  | 0.4124          | 0.8755   |
| 0.1993        | 59.0  | 767  | 0.4124          | 0.8755   |
| 0.2507        | 60.0  | 780  | 0.4118          | 0.8791   |
| 0.324         | 61.0  | 793  | 0.4101          | 0.8535   |
| 0.2303        | 62.0  | 806  | 0.4090          | 0.8718   |
| 0.2767        | 63.0  | 819  | 0.4072          | 0.8608   |
| 0.3318        | 64.0  | 832  | 0.4071          | 0.8681   |
| 0.1946        | 65.0  | 845  | 0.4064          | 0.8681   |
| 0.4204        | 66.0  | 858  | 0.4055          | 0.8608   |
| 0.3351        | 67.0  | 871  | 0.4031          | 0.8608   |
| 0.2772        | 68.0  | 884  | 0.4013          | 0.8645   |
| 0.2969        | 69.0  | 897  | 0.4000          | 0.8681   |
| 0.2755        | 70.0  | 910  | 0.4021          | 0.8901   |
| 0.2835        | 71.0  | 923  | 0.4005          | 0.8608   |
| 0.2487        | 72.0  | 936  | 0.3998          | 0.8608   |
| 0.2447        | 73.0  | 949  | 0.3987          | 0.8571   |
| 0.3512        | 74.0  | 962  | 0.3970          | 0.8718   |
| 0.2303        | 75.0  | 975  | 0.3975          | 0.8681   |
| 0.2271        | 76.0  | 988  | 0.3976          | 0.8791   |
| 0.2325        | 77.0  | 1001 | 0.3980          | 0.8938   |
| 0.2517        | 78.0  | 1014 | 0.3965          | 0.8901   |
| 0.2839        | 79.0  | 1027 | 0.3956          | 0.8938   |
| 0.1994        | 80.0  | 1040 | 0.3940          | 0.8828   |
| 0.4525        | 81.0  | 1053 | 0.3934          | 0.8864   |
| 0.2178        | 82.0  | 1066 | 0.3930          | 0.8828   |
| 0.2784        | 83.0  | 1079 | 0.3929          | 0.8901   |
| 0.1956        | 84.0  | 1092 | 0.3930          | 0.8901   |
| 0.2713        | 85.0  | 1105 | 0.3922          | 0.8828   |
| 0.2331        | 86.0  | 1118 | 0.3920          | 0.8828   |
| 0.3294        | 87.0  | 1131 | 0.3917          | 0.8864   |
| 0.2998        | 88.0  | 1144 | 0.3911          | 0.8864   |
| 0.3767        | 89.0  | 1157 | 0.3909          | 0.8864   |
| 0.3126        | 90.0  | 1170 | 0.3908          | 0.8828   |
| 0.2427        | 91.0  | 1183 | 0.3903          | 0.8791   |
| 0.2696        | 92.0  | 1196 | 0.3898          | 0.8828   |
| 0.2664        | 93.0  | 1209 | 0.3897          | 0.8828   |
| 0.3718        | 94.0  | 1222 | 0.3898          | 0.8828   |
| 0.2813        | 95.0  | 1235 | 0.3899          | 0.8828   |
| 0.3105        | 96.0  | 1248 | 0.3898          | 0.8828   |
| 0.2452        | 97.0  | 1261 | 0.3901          | 0.8828   |
| 0.2775        | 98.0  | 1274 | 0.3900          | 0.8828   |
| 0.3814        | 99.0  | 1287 | 0.3901          | 0.8828   |
| 0.2861        | 100.0 | 1300 | 0.3901          | 0.8828   |


### Framework versions

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

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