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# smids_1x_deit_small_rms_0001_fold1

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7301
- Accuracy: 0.6811

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1511        | 1.0   | 76   | 1.0039          | 0.4290   |
| 0.9396        | 2.0   | 152  | 0.9980          | 0.4658   |
| 0.9392        | 3.0   | 228  | 1.1592          | 0.3239   |
| 0.9832        | 4.0   | 304  | 1.0157          | 0.4791   |
| 0.9342        | 5.0   | 380  | 0.9184          | 0.4725   |
| 0.951         | 6.0   | 456  | 0.9262          | 0.4958   |
| 1.1061        | 7.0   | 532  | 1.1999          | 0.3406   |
| 0.8983        | 8.0   | 608  | 1.5626          | 0.4207   |
| 0.8399        | 9.0   | 684  | 0.8862          | 0.5242   |
| 0.7906        | 10.0  | 760  | 2.9194          | 0.3255   |
| 0.9054        | 11.0  | 836  | 0.8409          | 0.5476   |
| 0.8842        | 12.0  | 912  | 0.8563          | 0.5409   |
| 0.8173        | 13.0  | 988  | 0.9009          | 0.4958   |
| 0.8653        | 14.0  | 1064 | 0.8617          | 0.5476   |
| 0.7859        | 15.0  | 1140 | 0.8470          | 0.5109   |
| 0.7904        | 16.0  | 1216 | 0.8290          | 0.6027   |
| 0.8076        | 17.0  | 1292 | 1.0668          | 0.5326   |
| 0.7582        | 18.0  | 1368 | 0.8092          | 0.5776   |
| 0.8375        | 19.0  | 1444 | 0.8034          | 0.5927   |
| 0.817         | 20.0  | 1520 | 0.8094          | 0.5593   |
| 0.7636        | 21.0  | 1596 | 0.8786          | 0.6060   |
| 0.7574        | 22.0  | 1672 | 0.7805          | 0.6093   |
| 0.7196        | 23.0  | 1748 | 0.8013          | 0.6227   |
| 0.746         | 24.0  | 1824 | 0.9940          | 0.5492   |
| 0.698         | 25.0  | 1900 | 0.7894          | 0.6227   |
| 0.7416        | 26.0  | 1976 | 0.7704          | 0.6177   |
| 0.7441        | 27.0  | 2052 | 0.7868          | 0.6110   |
| 0.7488        | 28.0  | 2128 | 0.7854          | 0.6294   |
| 0.6844        | 29.0  | 2204 | 0.7483          | 0.6394   |
| 0.7046        | 30.0  | 2280 | 0.7522          | 0.6144   |
| 0.7612        | 31.0  | 2356 | 0.7237          | 0.6811   |
| 0.7095        | 32.0  | 2432 | 0.7781          | 0.6060   |
| 0.7219        | 33.0  | 2508 | 0.7248          | 0.6477   |
| 0.7697        | 34.0  | 2584 | 0.7404          | 0.6394   |
| 0.7924        | 35.0  | 2660 | 0.7779          | 0.6077   |
| 0.6939        | 36.0  | 2736 | 0.7018          | 0.6628   |
| 0.7175        | 37.0  | 2812 | 0.7115          | 0.6711   |
| 0.663         | 38.0  | 2888 | 0.7095          | 0.6594   |
| 0.7209        | 39.0  | 2964 | 0.7131          | 0.6761   |
| 0.6707        | 40.0  | 3040 | 0.7148          | 0.6745   |
| 0.6033        | 41.0  | 3116 | 0.7278          | 0.6761   |
| 0.6657        | 42.0  | 3192 | 0.7175          | 0.6745   |
| 0.5768        | 43.0  | 3268 | 0.7542          | 0.6611   |
| 0.608         | 44.0  | 3344 | 0.7272          | 0.6811   |
| 0.5917        | 45.0  | 3420 | 0.7194          | 0.6795   |
| 0.6179        | 46.0  | 3496 | 0.7229          | 0.6828   |
| 0.5513        | 47.0  | 3572 | 0.7301          | 0.6861   |
| 0.5669        | 48.0  | 3648 | 0.7286          | 0.6845   |
| 0.4852        | 49.0  | 3724 | 0.7286          | 0.6811   |
| 0.6153        | 50.0  | 3800 | 0.7301          | 0.6811   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm