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# smids_5x_deit_small_rms_001_fold3

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: 0.5638
- Accuracy: 0.7967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8408        | 1.0   | 375   | 0.8491          | 0.5383   |
| 0.836         | 2.0   | 750   | 0.8820          | 0.4983   |
| 0.8155        | 3.0   | 1125  | 0.8200          | 0.5917   |
| 0.829         | 4.0   | 1500  | 0.7980          | 0.5933   |
| 0.8032        | 5.0   | 1875  | 0.8027          | 0.5967   |
| 0.8095        | 6.0   | 2250  | 0.7557          | 0.635    |
| 0.7468        | 7.0   | 2625  | 0.7635          | 0.65     |
| 0.7133        | 8.0   | 3000  | 0.7025          | 0.6667   |
| 0.6424        | 9.0   | 3375  | 0.8608          | 0.6467   |
| 0.6511        | 10.0  | 3750  | 0.6834          | 0.6817   |
| 0.6928        | 11.0  | 4125  | 0.7883          | 0.6183   |
| 0.6757        | 12.0  | 4500  | 0.7380          | 0.635    |
| 0.6473        | 13.0  | 4875  | 0.6942          | 0.6633   |
| 0.5828        | 14.0  | 5250  | 0.6863          | 0.7117   |
| 0.5787        | 15.0  | 5625  | 0.6877          | 0.6933   |
| 0.5711        | 16.0  | 6000  | 0.7012          | 0.685    |
| 0.6198        | 17.0  | 6375  | 0.6000          | 0.7183   |
| 0.6331        | 18.0  | 6750  | 0.6316          | 0.7217   |
| 0.5457        | 19.0  | 7125  | 0.6381          | 0.7333   |
| 0.585         | 20.0  | 7500  | 0.6083          | 0.7367   |
| 0.4779        | 21.0  | 7875  | 0.6292          | 0.7      |
| 0.4504        | 22.0  | 8250  | 0.5995          | 0.7533   |
| 0.513         | 23.0  | 8625  | 0.6005          | 0.735    |
| 0.5931        | 24.0  | 9000  | 0.5450          | 0.76     |
| 0.4836        | 25.0  | 9375  | 0.5749          | 0.7517   |
| 0.4981        | 26.0  | 9750  | 0.5577          | 0.77     |
| 0.5035        | 27.0  | 10125 | 0.5452          | 0.7583   |
| 0.4996        | 28.0  | 10500 | 0.5583          | 0.765    |
| 0.4767        | 29.0  | 10875 | 0.5589          | 0.765    |
| 0.4202        | 30.0  | 11250 | 0.5291          | 0.78     |
| 0.4307        | 31.0  | 11625 | 0.5250          | 0.7967   |
| 0.5107        | 32.0  | 12000 | 0.5223          | 0.7917   |
| 0.4923        | 33.0  | 12375 | 0.5101          | 0.7917   |
| 0.4996        | 34.0  | 12750 | 0.5329          | 0.79     |
| 0.3762        | 35.0  | 13125 | 0.5542          | 0.79     |
| 0.4379        | 36.0  | 13500 | 0.5598          | 0.7883   |
| 0.4018        | 37.0  | 13875 | 0.5521          | 0.7983   |
| 0.4033        | 38.0  | 14250 | 0.5506          | 0.7767   |
| 0.4228        | 39.0  | 14625 | 0.5150          | 0.7917   |
| 0.366         | 40.0  | 15000 | 0.5580          | 0.8017   |
| 0.3549        | 41.0  | 15375 | 0.5360          | 0.8067   |
| 0.3677        | 42.0  | 15750 | 0.5521          | 0.8      |
| 0.4255        | 43.0  | 16125 | 0.5412          | 0.8033   |
| 0.355         | 44.0  | 16500 | 0.5640          | 0.7717   |
| 0.3586        | 45.0  | 16875 | 0.5441          | 0.7783   |
| 0.3404        | 46.0  | 17250 | 0.5592          | 0.7867   |
| 0.3867        | 47.0  | 17625 | 0.5593          | 0.8      |
| 0.3586        | 48.0  | 18000 | 0.5571          | 0.8067   |
| 0.2696        | 49.0  | 18375 | 0.5541          | 0.8      |
| 0.3761        | 50.0  | 18750 | 0.5638          | 0.7967   |


### 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