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# smids_3x_deit_small_sgd_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: 0.5893
- Accuracy: 0.7546

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0679        | 1.0   | 226   | 1.0513          | 0.4491   |
| 1.0307        | 2.0   | 452   | 1.0262          | 0.4841   |
| 0.9834        | 3.0   | 678   | 1.0025          | 0.5309   |
| 0.9773        | 4.0   | 904   | 0.9789          | 0.5526   |
| 0.9692        | 5.0   | 1130  | 0.9560          | 0.5710   |
| 0.9555        | 6.0   | 1356  | 0.9334          | 0.5843   |
| 0.9069        | 7.0   | 1582  | 0.9110          | 0.6077   |
| 0.8993        | 8.0   | 1808  | 0.8899          | 0.6210   |
| 0.8781        | 9.0   | 2034  | 0.8696          | 0.6327   |
| 0.881         | 10.0  | 2260  | 0.8502          | 0.6511   |
| 0.8393        | 11.0  | 2486  | 0.8314          | 0.6661   |
| 0.8206        | 12.0  | 2712  | 0.8136          | 0.6694   |
| 0.7291        | 13.0  | 2938  | 0.7969          | 0.6661   |
| 0.7685        | 14.0  | 3164  | 0.7808          | 0.6728   |
| 0.7796        | 15.0  | 3390  | 0.7656          | 0.6795   |
| 0.7626        | 16.0  | 3616  | 0.7511          | 0.6845   |
| 0.6907        | 17.0  | 3842  | 0.7376          | 0.6962   |
| 0.7127        | 18.0  | 4068  | 0.7250          | 0.7028   |
| 0.7138        | 19.0  | 4294  | 0.7133          | 0.7078   |
| 0.6728        | 20.0  | 4520  | 0.7022          | 0.7179   |
| 0.6631        | 21.0  | 4746  | 0.6922          | 0.7229   |
| 0.6986        | 22.0  | 4972  | 0.6830          | 0.7312   |
| 0.6948        | 23.0  | 5198  | 0.6744          | 0.7346   |
| 0.6653        | 24.0  | 5424  | 0.6664          | 0.7396   |
| 0.6494        | 25.0  | 5650  | 0.6590          | 0.7446   |
| 0.68          | 26.0  | 5876  | 0.6520          | 0.7462   |
| 0.5871        | 27.0  | 6102  | 0.6459          | 0.7462   |
| 0.6576        | 28.0  | 6328  | 0.6401          | 0.7479   |
| 0.5996        | 29.0  | 6554  | 0.6347          | 0.7496   |
| 0.6519        | 30.0  | 6780  | 0.6298          | 0.7496   |
| 0.6001        | 31.0  | 7006  | 0.6252          | 0.7496   |
| 0.6281        | 32.0  | 7232  | 0.6210          | 0.7479   |
| 0.555         | 33.0  | 7458  | 0.6170          | 0.7479   |
| 0.5831        | 34.0  | 7684  | 0.6134          | 0.7513   |
| 0.6219        | 35.0  | 7910  | 0.6102          | 0.7496   |
| 0.5515        | 36.0  | 8136  | 0.6072          | 0.7496   |
| 0.5829        | 37.0  | 8362  | 0.6045          | 0.7513   |
| 0.6171        | 38.0  | 8588  | 0.6020          | 0.7513   |
| 0.5762        | 39.0  | 8814  | 0.5998          | 0.7513   |
| 0.5768        | 40.0  | 9040  | 0.5978          | 0.7513   |
| 0.5572        | 41.0  | 9266  | 0.5960          | 0.7513   |
| 0.5838        | 42.0  | 9492  | 0.5945          | 0.7513   |
| 0.5735        | 43.0  | 9718  | 0.5931          | 0.7513   |
| 0.5285        | 44.0  | 9944  | 0.5920          | 0.7513   |
| 0.6211        | 45.0  | 10170 | 0.5911          | 0.7513   |
| 0.5665        | 46.0  | 10396 | 0.5904          | 0.7546   |
| 0.5672        | 47.0  | 10622 | 0.5899          | 0.7563   |
| 0.562         | 48.0  | 10848 | 0.5895          | 0.7546   |
| 0.5485        | 49.0  | 11074 | 0.5894          | 0.7546   |
| 0.5589        | 50.0  | 11300 | 0.5893          | 0.7546   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm