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# smids_1x_deit_tiny_rms_001_fold1

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2760
- Accuracy: 0.7479

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1876        | 1.0   | 76   | 1.3425          | 0.3356   |
| 1.1463        | 2.0   | 152  | 1.2111          | 0.3356   |
| 1.0954        | 3.0   | 228  | 1.0384          | 0.4908   |
| 0.9283        | 4.0   | 304  | 1.3266          | 0.3422   |
| 0.8828        | 5.0   | 380  | 0.8561          | 0.5609   |
| 0.9197        | 6.0   | 456  | 0.8658          | 0.5326   |
| 0.8712        | 7.0   | 532  | 0.8805          | 0.5242   |
| 0.8491        | 8.0   | 608  | 0.9865          | 0.5476   |
| 0.8029        | 9.0   | 684  | 0.8775          | 0.5609   |
| 0.7757        | 10.0  | 760  | 1.7456          | 0.4307   |
| 0.873         | 11.0  | 836  | 0.8686          | 0.5058   |
| 0.9608        | 12.0  | 912  | 0.7919          | 0.5893   |
| 0.7695        | 13.0  | 988  | 0.8046          | 0.5659   |
| 0.7753        | 14.0  | 1064 | 0.7823          | 0.5927   |
| 0.7166        | 15.0  | 1140 | 0.8383          | 0.5576   |
| 0.7186        | 16.0  | 1216 | 0.7798          | 0.6444   |
| 0.7445        | 17.0  | 1292 | 0.7722          | 0.6427   |
| 0.6602        | 18.0  | 1368 | 0.7838          | 0.6227   |
| 0.7252        | 19.0  | 1444 | 0.6881          | 0.6945   |
| 0.8232        | 20.0  | 1520 | 0.7249          | 0.6845   |
| 0.6331        | 21.0  | 1596 | 0.7098          | 0.6795   |
| 0.5883        | 22.0  | 1672 | 0.7178          | 0.6711   |
| 0.6215        | 23.0  | 1748 | 0.7089          | 0.6912   |
| 0.6142        | 24.0  | 1824 | 0.6619          | 0.7062   |
| 0.5494        | 25.0  | 1900 | 0.6904          | 0.7212   |
| 0.6135        | 26.0  | 1976 | 0.6508          | 0.7129   |
| 0.5009        | 27.0  | 2052 | 0.6608          | 0.7212   |
| 0.587         | 28.0  | 2128 | 0.7282          | 0.7112   |
| 0.4496        | 29.0  | 2204 | 0.6988          | 0.7095   |
| 0.4737        | 30.0  | 2280 | 0.7982          | 0.6728   |
| 0.5606        | 31.0  | 2356 | 0.7102          | 0.7062   |
| 0.5118        | 32.0  | 2432 | 0.6558          | 0.7479   |
| 0.4696        | 33.0  | 2508 | 0.9166          | 0.6978   |
| 0.5104        | 34.0  | 2584 | 0.7689          | 0.6995   |
| 0.4795        | 35.0  | 2660 | 0.6827          | 0.7362   |
| 0.432         | 36.0  | 2736 | 0.7633          | 0.7129   |
| 0.4887        | 37.0  | 2812 | 0.7070          | 0.7162   |
| 0.4102        | 38.0  | 2888 | 0.7227          | 0.7396   |
| 0.3613        | 39.0  | 2964 | 0.7184          | 0.7329   |
| 0.3288        | 40.0  | 3040 | 0.7694          | 0.7379   |
| 0.3339        | 41.0  | 3116 | 0.7145          | 0.7429   |
| 0.242         | 42.0  | 3192 | 0.8275          | 0.7429   |
| 0.3299        | 43.0  | 3268 | 0.8614          | 0.7446   |
| 0.2932        | 44.0  | 3344 | 0.9021          | 0.7362   |
| 0.2403        | 45.0  | 3420 | 0.9785          | 0.7479   |
| 0.1305        | 46.0  | 3496 | 1.0696          | 0.7546   |
| 0.1845        | 47.0  | 3572 | 1.1023          | 0.7529   |
| 0.1407        | 48.0  | 3648 | 1.2188          | 0.7513   |
| 0.0723        | 49.0  | 3724 | 1.2702          | 0.7479   |
| 0.0363        | 50.0  | 3800 | 1.2760          | 0.7479   |


### 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