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# vit-msn-small-wbc-classifier-0316-cleandataset-10

This model is a fine-tuned version of [Melo1512/vit-msn-small-wbc-classifier-0316-cleandataset-10](https://huggingface.co/Melo1512/vit-msn-small-wbc-classifier-0316-cleandataset-10) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3943
- Accuracy: 0.8599

## 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: 1e-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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.3785        | 0.9730  | 18   | 0.3985          | 0.8569   |
| 0.3432        | 2.0     | 37   | 0.3996          | 0.8557   |
| 0.3454        | 2.9730  | 55   | 0.4011          | 0.8553   |
| 0.3639        | 4.0     | 74   | 0.4034          | 0.8538   |
| 0.3544        | 4.9730  | 92   | 0.4049          | 0.8546   |
| 0.3607        | 6.0     | 111  | 0.4057          | 0.8538   |
| 0.3652        | 6.9730  | 129  | 0.4046          | 0.8561   |
| 0.3639        | 8.0     | 148  | 0.4046          | 0.8553   |
| 0.3472        | 8.9730  | 166  | 0.4048          | 0.8561   |
| 0.3704        | 10.0    | 185  | 0.4033          | 0.8546   |
| 0.3954        | 10.9730 | 203  | 0.4009          | 0.8565   |
| 0.372         | 12.0    | 222  | 0.4022          | 0.8546   |
| 0.3599        | 12.9730 | 240  | 0.4005          | 0.8561   |
| 0.3689        | 14.0    | 259  | 0.4018          | 0.8550   |
| 0.3687        | 14.9730 | 277  | 0.4016          | 0.8553   |
| 0.3521        | 16.0    | 296  | 0.4000          | 0.8561   |
| 0.3817        | 16.9730 | 314  | 0.4001          | 0.8553   |
| 0.3768        | 18.0    | 333  | 0.3994          | 0.8550   |
| 0.3835        | 18.9730 | 351  | 0.4041          | 0.8546   |
| 0.3833        | 20.0    | 370  | 0.4042          | 0.8553   |
| 0.36          | 20.9730 | 388  | 0.4012          | 0.8561   |
| 0.3729        | 22.0    | 407  | 0.4023          | 0.8565   |
| 0.3647        | 22.9730 | 425  | 0.4029          | 0.8546   |
| 0.3811        | 24.0    | 444  | 0.4011          | 0.8561   |
| 0.38          | 24.9730 | 462  | 0.3999          | 0.8569   |
| 0.3588        | 26.0    | 481  | 0.3994          | 0.8557   |
| 0.3554        | 26.9730 | 499  | 0.3991          | 0.8561   |
| 0.354         | 28.0    | 518  | 0.3995          | 0.8561   |
| 0.3577        | 28.9730 | 536  | 0.3986          | 0.8557   |
| 0.3723        | 30.0    | 555  | 0.3998          | 0.8561   |
| 0.3763        | 30.9730 | 573  | 0.3994          | 0.8561   |
| 0.3701        | 32.0    | 592  | 0.3994          | 0.8569   |
| 0.3728        | 32.9730 | 610  | 0.3980          | 0.8553   |
| 0.3649        | 34.0    | 629  | 0.3964          | 0.8565   |
| 0.3551        | 34.9730 | 647  | 0.3982          | 0.8569   |
| 0.3832        | 36.0    | 666  | 0.3977          | 0.8576   |
| 0.3459        | 36.9730 | 684  | 0.3968          | 0.8561   |
| 0.3613        | 38.0    | 703  | 0.3966          | 0.8561   |
| 0.3588        | 38.9730 | 721  | 0.3968          | 0.8565   |
| 0.3483        | 40.0    | 740  | 0.3958          | 0.8573   |
| 0.3693        | 40.9730 | 758  | 0.3967          | 0.8576   |
| 0.3544        | 42.0    | 777  | 0.3988          | 0.8576   |
| 0.3701        | 42.9730 | 795  | 0.3976          | 0.8573   |
| 0.3649        | 44.0    | 814  | 0.3984          | 0.8565   |
| 0.3621        | 44.9730 | 832  | 0.3966          | 0.8573   |
| 0.3494        | 46.0    | 851  | 0.3989          | 0.8573   |
| 0.373         | 46.9730 | 869  | 0.3993          | 0.8573   |
| 0.3911        | 48.0    | 888  | 0.3978          | 0.8576   |
| 0.3716        | 48.9730 | 906  | 0.3967          | 0.8576   |
| 0.3685        | 50.0    | 925  | 0.3968          | 0.8576   |
| 0.3879        | 50.9730 | 943  | 0.3950          | 0.8573   |
| 0.3774        | 52.0    | 962  | 0.3951          | 0.8580   |
| 0.3588        | 52.9730 | 980  | 0.3950          | 0.8584   |
| 0.3746        | 54.0    | 999  | 0.3959          | 0.8584   |
| 0.3677        | 54.9730 | 1017 | 0.3960          | 0.8584   |
| 0.3608        | 56.0    | 1036 | 0.3965          | 0.8588   |
| 0.3518        | 56.9730 | 1054 | 0.3963          | 0.8580   |
| 0.3554        | 58.0    | 1073 | 0.3957          | 0.8588   |
| 0.3584        | 58.9730 | 1091 | 0.3957          | 0.8584   |
| 0.3776        | 60.0    | 1110 | 0.3948          | 0.8592   |
| 0.364         | 60.9730 | 1128 | 0.3942          | 0.8588   |
| 0.3647        | 62.0    | 1147 | 0.3942          | 0.8584   |
| 0.3613        | 62.9730 | 1165 | 0.3949          | 0.8588   |
| 0.3509        | 64.0    | 1184 | 0.3961          | 0.8584   |
| 0.3816        | 64.9730 | 1202 | 0.3967          | 0.8584   |
| 0.3552        | 66.0    | 1221 | 0.3957          | 0.8588   |
| 0.3461        | 66.9730 | 1239 | 0.3946          | 0.8588   |
| 0.364         | 68.0    | 1258 | 0.3940          | 0.8588   |
| 0.372         | 68.9730 | 1276 | 0.3943          | 0.8599   |
| 0.347         | 70.0    | 1295 | 0.3939          | 0.8592   |
| 0.3537        | 70.9730 | 1313 | 0.3943          | 0.8599   |
| 0.3537        | 72.0    | 1332 | 0.3950          | 0.8595   |
| 0.3823        | 72.9730 | 1350 | 0.3951          | 0.8592   |
| 0.3454        | 74.0    | 1369 | 0.3947          | 0.8592   |
| 0.3667        | 74.9730 | 1387 | 0.3949          | 0.8592   |
| 0.3585        | 76.0    | 1406 | 0.3945          | 0.8592   |
| 0.356         | 76.9730 | 1424 | 0.3947          | 0.8592   |
| 0.337         | 78.0    | 1443 | 0.3949          | 0.8592   |
| 0.3588        | 78.9730 | 1461 | 0.3944          | 0.8592   |
| 0.3591        | 80.0    | 1480 | 0.3941          | 0.8592   |
| 0.3638        | 80.9730 | 1498 | 0.3943          | 0.8592   |
| 0.367         | 82.0    | 1517 | 0.3941          | 0.8592   |
| 0.3694        | 82.9730 | 1535 | 0.3943          | 0.8592   |
| 0.3779        | 84.0    | 1554 | 0.3941          | 0.8592   |
| 0.344         | 84.9730 | 1572 | 0.3939          | 0.8595   |
| 0.3619        | 86.0    | 1591 | 0.3935          | 0.8592   |
| 0.342         | 86.9730 | 1609 | 0.3934          | 0.8595   |
| 0.3686        | 88.0    | 1628 | 0.3931          | 0.8595   |
| 0.3407        | 88.9730 | 1646 | 0.3931          | 0.8595   |
| 0.3553        | 90.0    | 1665 | 0.3933          | 0.8599   |
| 0.367         | 90.9730 | 1683 | 0.3934          | 0.8595   |
| 0.3665        | 92.0    | 1702 | 0.3932          | 0.8599   |
| 0.3684        | 92.9730 | 1720 | 0.3932          | 0.8599   |
| 0.3685        | 94.0    | 1739 | 0.3934          | 0.8595   |
| 0.375         | 94.9730 | 1757 | 0.3934          | 0.8592   |
| 0.3564        | 96.0    | 1776 | 0.3934          | 0.8592   |
| 0.362         | 96.9730 | 1794 | 0.3934          | 0.8592   |
| 0.3688        | 97.2973 | 1800 | 0.3934          | 0.8592   |


### Framework versions

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

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