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# smids_5x_deit_tiny_sgd_00001_fold2

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: 1.0573
- Accuracy: 0.4476

## 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-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3977        | 1.0   | 375   | 1.3189          | 0.3428   |
| 1.3124        | 2.0   | 750   | 1.2870          | 0.3411   |
| 1.2542        | 3.0   | 1125  | 1.2595          | 0.3378   |
| 1.2046        | 4.0   | 1500  | 1.2361          | 0.3478   |
| 1.2563        | 5.0   | 1875  | 1.2165          | 0.3544   |
| 1.2759        | 6.0   | 2250  | 1.1999          | 0.3561   |
| 1.1771        | 7.0   | 2625  | 1.1858          | 0.3527   |
| 1.1858        | 8.0   | 3000  | 1.1739          | 0.3710   |
| 1.1713        | 9.0   | 3375  | 1.1636          | 0.3644   |
| 1.1774        | 10.0  | 3750  | 1.1549          | 0.3760   |
| 1.1522        | 11.0  | 4125  | 1.1472          | 0.3760   |
| 1.1182        | 12.0  | 4500  | 1.1403          | 0.3744   |
| 1.1161        | 13.0  | 4875  | 1.1344          | 0.3827   |
| 1.1676        | 14.0  | 5250  | 1.1289          | 0.3827   |
| 1.1382        | 15.0  | 5625  | 1.1238          | 0.3860   |
| 1.129         | 16.0  | 6000  | 1.1191          | 0.3943   |
| 1.1144        | 17.0  | 6375  | 1.1146          | 0.3910   |
| 1.1043        | 18.0  | 6750  | 1.1105          | 0.3894   |
| 1.1008        | 19.0  | 7125  | 1.1065          | 0.3960   |
| 1.1097        | 20.0  | 7500  | 1.1028          | 0.4077   |
| 1.1084        | 21.0  | 7875  | 1.0993          | 0.4093   |
| 1.0777        | 22.0  | 8250  | 1.0960          | 0.4110   |
| 1.0857        | 23.0  | 8625  | 1.0928          | 0.4126   |
| 1.096         | 24.0  | 9000  | 1.0898          | 0.4126   |
| 1.1016        | 25.0  | 9375  | 1.0869          | 0.4176   |
| 1.0637        | 26.0  | 9750  | 1.0843          | 0.4226   |
| 1.0804        | 27.0  | 10125 | 1.0817          | 0.4226   |
| 1.0961        | 28.0  | 10500 | 1.0793          | 0.4226   |
| 1.0888        | 29.0  | 10875 | 1.0771          | 0.4293   |
| 1.0508        | 30.0  | 11250 | 1.0750          | 0.4293   |
| 1.0685        | 31.0  | 11625 | 1.0730          | 0.4326   |
| 1.1026        | 32.0  | 12000 | 1.0712          | 0.4309   |
| 1.0612        | 33.0  | 12375 | 1.0694          | 0.4359   |
| 1.0734        | 34.0  | 12750 | 1.0679          | 0.4393   |
| 1.0868        | 35.0  | 13125 | 1.0664          | 0.4393   |
| 1.0597        | 36.0  | 13500 | 1.0650          | 0.4393   |
| 1.0653        | 37.0  | 13875 | 1.0638          | 0.4409   |
| 1.0598        | 38.0  | 14250 | 1.0627          | 0.4443   |
| 1.0773        | 39.0  | 14625 | 1.0617          | 0.4443   |
| 1.0819        | 40.0  | 15000 | 1.0608          | 0.4443   |
| 1.0608        | 41.0  | 15375 | 1.0600          | 0.4459   |
| 1.0652        | 42.0  | 15750 | 1.0594          | 0.4459   |
| 1.04          | 43.0  | 16125 | 1.0588          | 0.4476   |
| 1.0518        | 44.0  | 16500 | 1.0583          | 0.4476   |
| 1.0814        | 45.0  | 16875 | 1.0580          | 0.4476   |
| 1.0536        | 46.0  | 17250 | 1.0577          | 0.4476   |
| 1.0612        | 47.0  | 17625 | 1.0575          | 0.4476   |
| 1.0833        | 48.0  | 18000 | 1.0574          | 0.4476   |
| 1.0816        | 49.0  | 18375 | 1.0573          | 0.4476   |
| 1.0754        | 50.0  | 18750 | 1.0573          | 0.4476   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm