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# vit-msn-small-wbc-classifier-100

This model is a fine-tuned version of [Melo1512/vit-msn-small-wbc-blur-detector](https://huggingface.co/Melo1512/vit-msn-small-wbc-blur-detector) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2005
- Accuracy: 0.9272

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2356        | 1.0   | 208   | 0.2005          | 0.9272   |
| 0.2305        | 2.0   | 416   | 0.2259          | 0.9195   |
| 0.246         | 3.0   | 624   | 0.2097          | 0.9210   |
| 0.2585        | 4.0   | 832   | 0.2184          | 0.9180   |
| 0.2593        | 5.0   | 1040  | 0.2331          | 0.9171   |
| 0.2483        | 6.0   | 1248  | 0.2170          | 0.9198   |
| 0.268         | 7.0   | 1456  | 0.2228          | 0.9181   |
| 0.3112        | 8.0   | 1664  | 0.2361          | 0.9171   |
| 0.2679        | 9.0   | 1872  | 0.2273          | 0.9185   |
| 0.3099        | 10.0  | 2080  | 0.2303          | 0.9144   |
| 0.2749        | 11.0  | 2288  | 0.2658          | 0.9125   |
| 0.2475        | 12.0  | 2496  | 0.2247          | 0.9179   |
| 0.2338        | 13.0  | 2704  | 0.2333          | 0.9139   |
| 0.2731        | 14.0  | 2912  | 0.2295          | 0.9153   |
| 0.229         | 15.0  | 3120  | 0.2505          | 0.9138   |
| 0.2462        | 16.0  | 3328  | 0.2534          | 0.9137   |
| 0.2274        | 17.0  | 3536  | 0.2652          | 0.9079   |
| 0.2339        | 18.0  | 3744  | 0.2550          | 0.9153   |
| 0.2053        | 19.0  | 3952  | 0.2819          | 0.9106   |
| 0.2063        | 20.0  | 4160  | 0.2747          | 0.9129   |
| 0.1964        | 21.0  | 4368  | 0.2975          | 0.9118   |
| 0.1953        | 22.0  | 4576  | 0.2799          | 0.9145   |
| 0.1938        | 23.0  | 4784  | 0.3197          | 0.9100   |
| 0.1851        | 24.0  | 4992  | 0.3143          | 0.9138   |
| 0.1931        | 25.0  | 5200  | 0.3331          | 0.9125   |
| 0.1877        | 26.0  | 5408  | 0.3044          | 0.9110   |
| 0.177         | 27.0  | 5616  | 0.3271          | 0.9109   |
| 0.1529        | 28.0  | 5824  | 0.3382          | 0.9094   |
| 0.1684        | 29.0  | 6032  | 0.3415          | 0.9128   |
| 0.176         | 30.0  | 6240  | 0.3463          | 0.9095   |
| 0.1496        | 31.0  | 6448  | 0.3952          | 0.9136   |
| 0.1509        | 32.0  | 6656  | 0.3690          | 0.9121   |
| 0.1463        | 33.0  | 6864  | 0.3999          | 0.9094   |
| 0.1354        | 34.0  | 7072  | 0.3996          | 0.9135   |
| 0.1546        | 35.0  | 7280  | 0.3810          | 0.9116   |
| 0.1513        | 36.0  | 7488  | 0.3992          | 0.9121   |
| 0.115         | 37.0  | 7696  | 0.4295          | 0.9132   |
| 0.1479        | 38.0  | 7904  | 0.4363          | 0.9123   |
| 0.1455        | 39.0  | 8112  | 0.4220          | 0.9140   |
| 0.1353        | 40.0  | 8320  | 0.4112          | 0.9127   |
| 0.141         | 41.0  | 8528  | 0.4322          | 0.9139   |
| 0.1272        | 42.0  | 8736  | 0.4176          | 0.9119   |
| 0.1402        | 43.0  | 8944  | 0.4041          | 0.9108   |
| 0.1236        | 44.0  | 9152  | 0.4478          | 0.9095   |
| 0.1349        | 45.0  | 9360  | 0.4211          | 0.9112   |
| 0.1472        | 46.0  | 9568  | 0.4510          | 0.9113   |
| 0.1115        | 47.0  | 9776  | 0.4373          | 0.9119   |
| 0.1122        | 48.0  | 9984  | 0.4689          | 0.9129   |
| 0.1297        | 49.0  | 10192 | 0.4569          | 0.9140   |
| 0.1337        | 50.0  | 10400 | 0.4622          | 0.9111   |
| 0.1194        | 51.0  | 10608 | 0.4579          | 0.9151   |
| 0.1322        | 52.0  | 10816 | 0.4728          | 0.9104   |
| 0.1179        | 53.0  | 11024 | 0.4729          | 0.9125   |
| 0.1216        | 54.0  | 11232 | 0.5199          | 0.9114   |
| 0.1234        | 55.0  | 11440 | 0.4769          | 0.9135   |
| 0.1125        | 56.0  | 11648 | 0.4871          | 0.9118   |
| 0.1234        | 57.0  | 11856 | 0.4667          | 0.9146   |
| 0.1103        | 58.0  | 12064 | 0.4741          | 0.9119   |
| 0.1103        | 59.0  | 12272 | 0.4864          | 0.9129   |
| 0.1222        | 60.0  | 12480 | 0.4550          | 0.9143   |
| 0.127         | 61.0  | 12688 | 0.4919          | 0.9135   |
| 0.1117        | 62.0  | 12896 | 0.4946          | 0.9139   |
| 0.1078        | 63.0  | 13104 | 0.5040          | 0.9133   |
| 0.1127        | 64.0  | 13312 | 0.4804          | 0.9126   |
| 0.1122        | 65.0  | 13520 | 0.4997          | 0.9136   |
| 0.1089        | 66.0  | 13728 | 0.5134          | 0.9139   |
| 0.1179        | 67.0  | 13936 | 0.5246          | 0.9155   |
| 0.0934        | 68.0  | 14144 | 0.5158          | 0.9126   |
| 0.1011        | 69.0  | 14352 | 0.5361          | 0.9140   |
| 0.1063        | 70.0  | 14560 | 0.5326          | 0.9135   |
| 0.1021        | 71.0  | 14768 | 0.5151          | 0.9143   |
| 0.1007        | 72.0  | 14976 | 0.5390          | 0.9143   |
| 0.0946        | 73.0  | 15184 | 0.5256          | 0.9114   |
| 0.097         | 74.0  | 15392 | 0.5247          | 0.9135   |
| 0.0967        | 75.0  | 15600 | 0.5154          | 0.9144   |
| 0.0985        | 76.0  | 15808 | 0.5412          | 0.9154   |
| 0.0856        | 77.0  | 16016 | 0.5335          | 0.9148   |
| 0.103         | 78.0  | 16224 | 0.5210          | 0.9162   |
| 0.1033        | 79.0  | 16432 | 0.5165          | 0.9156   |
| 0.109         | 80.0  | 16640 | 0.5303          | 0.9150   |
| 0.0999        | 81.0  | 16848 | 0.5299          | 0.9158   |
| 0.0966        | 82.0  | 17056 | 0.5324          | 0.9167   |
| 0.0952        | 83.0  | 17264 | 0.5229          | 0.9168   |
| 0.1071        | 84.0  | 17472 | 0.5303          | 0.9176   |
| 0.0899        | 85.0  | 17680 | 0.5228          | 0.9160   |
| 0.0868        | 86.0  | 17888 | 0.5297          | 0.9149   |
| 0.1011        | 87.0  | 18096 | 0.5370          | 0.9156   |
| 0.0867        | 88.0  | 18304 | 0.5430          | 0.9158   |
| 0.0936        | 89.0  | 18512 | 0.5346          | 0.9165   |
| 0.0929        | 90.0  | 18720 | 0.5387          | 0.9163   |
| 0.0792        | 91.0  | 18928 | 0.5459          | 0.9150   |
| 0.0918        | 92.0  | 19136 | 0.5257          | 0.9165   |
| 0.0853        | 93.0  | 19344 | 0.5426          | 0.9155   |
| 0.0908        | 94.0  | 19552 | 0.5429          | 0.9153   |
| 0.0981        | 95.0  | 19760 | 0.5394          | 0.9155   |
| 0.0825        | 96.0  | 19968 | 0.5345          | 0.9168   |
| 0.0849        | 97.0  | 20176 | 0.5388          | 0.9164   |
| 0.0992        | 98.0  | 20384 | 0.5357          | 0.9168   |
| 0.0909        | 99.0  | 20592 | 0.5375          | 0.9167   |
| 0.0861        | 100.0 | 20800 | 0.5372          | 0.9166   |


### Framework versions

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

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