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# smids_5x_deit_base_rms_001_fold4

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7070
- Accuracy: 0.8

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.11          | 1.0   | 375   | 1.0993          | 0.325    |
| 0.9143        | 2.0   | 750   | 0.8093          | 0.5633   |
| 0.8119        | 3.0   | 1125  | 0.7905          | 0.58     |
| 0.8256        | 4.0   | 1500  | 0.7664          | 0.5933   |
| 0.7841        | 5.0   | 1875  | 0.7359          | 0.6383   |
| 0.7919        | 6.0   | 2250  | 0.7445          | 0.6033   |
| 0.7749        | 7.0   | 2625  | 0.6951          | 0.6567   |
| 0.741         | 8.0   | 3000  | 0.7702          | 0.6017   |
| 0.6745        | 9.0   | 3375  | 0.6771          | 0.6967   |
| 0.6495        | 10.0  | 3750  | 0.6304          | 0.7267   |
| 0.6291        | 11.0  | 4125  | 0.6937          | 0.665    |
| 0.6399        | 12.0  | 4500  | 0.6515          | 0.7017   |
| 0.559         | 13.0  | 4875  | 0.7230          | 0.6783   |
| 0.6232        | 14.0  | 5250  | 0.5711          | 0.76     |
| 0.6438        | 15.0  | 5625  | 0.5759          | 0.725    |
| 0.5708        | 16.0  | 6000  | 0.5672          | 0.7433   |
| 0.5817        | 17.0  | 6375  | 0.5983          | 0.7367   |
| 0.5534        | 18.0  | 6750  | 0.5595          | 0.7633   |
| 0.5539        | 19.0  | 7125  | 0.5109          | 0.7767   |
| 0.5681        | 20.0  | 7500  | 0.5115          | 0.7667   |
| 0.481         | 21.0  | 7875  | 0.5451          | 0.765    |
| 0.5126        | 22.0  | 8250  | 0.5810          | 0.7383   |
| 0.5213        | 23.0  | 8625  | 0.5434          | 0.7467   |
| 0.4811        | 24.0  | 9000  | 0.5453          | 0.7833   |
| 0.4831        | 25.0  | 9375  | 0.5703          | 0.7617   |
| 0.4515        | 26.0  | 9750  | 0.5020          | 0.795    |
| 0.4363        | 27.0  | 10125 | 0.5213          | 0.7733   |
| 0.4104        | 28.0  | 10500 | 0.5450          | 0.76     |
| 0.4526        | 29.0  | 10875 | 0.5543          | 0.76     |
| 0.4959        | 30.0  | 11250 | 0.5151          | 0.7683   |
| 0.5018        | 31.0  | 11625 | 0.5269          | 0.7933   |
| 0.4267        | 32.0  | 12000 | 0.5138          | 0.7933   |
| 0.464         | 33.0  | 12375 | 0.5486          | 0.7883   |
| 0.4612        | 34.0  | 12750 | 0.5500          | 0.7867   |
| 0.3667        | 35.0  | 13125 | 0.5319          | 0.7983   |
| 0.3692        | 36.0  | 13500 | 0.6112          | 0.77     |
| 0.443         | 37.0  | 13875 | 0.5427          | 0.8      |
| 0.431         | 38.0  | 14250 | 0.5062          | 0.7967   |
| 0.3837        | 39.0  | 14625 | 0.5492          | 0.7867   |
| 0.421         | 40.0  | 15000 | 0.6140          | 0.7883   |
| 0.3456        | 41.0  | 15375 | 0.6505          | 0.7817   |
| 0.3528        | 42.0  | 15750 | 0.6246          | 0.7733   |
| 0.383         | 43.0  | 16125 | 0.6178          | 0.7933   |
| 0.3235        | 44.0  | 16500 | 0.6286          | 0.7933   |
| 0.3165        | 45.0  | 16875 | 0.6316          | 0.7933   |
| 0.3289        | 46.0  | 17250 | 0.6422          | 0.7933   |
| 0.3499        | 47.0  | 17625 | 0.6701          | 0.8      |
| 0.2803        | 48.0  | 18000 | 0.6802          | 0.7917   |
| 0.279         | 49.0  | 18375 | 0.6961          | 0.8017   |
| 0.2671        | 50.0  | 18750 | 0.7070          | 0.8      |


### 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