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# smids_1x_deit_small_rms_0001_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: 1.1553
- Accuracy: 0.7017

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.14          | 1.0   | 75   | 1.1120          | 0.335    |
| 1.2072        | 2.0   | 150  | 1.0986          | 0.3333   |
| 0.9539        | 3.0   | 225  | 0.9334          | 0.4917   |
| 0.9512        | 4.0   | 300  | 0.9203          | 0.4983   |
| 0.911         | 5.0   | 375  | 1.0159          | 0.445    |
| 0.9061        | 6.0   | 450  | 0.9432          | 0.5133   |
| 0.8557        | 7.0   | 525  | 0.9707          | 0.5517   |
| 0.796         | 8.0   | 600  | 0.8853          | 0.5633   |
| 0.837         | 9.0   | 675  | 0.8169          | 0.5667   |
| 0.8343        | 10.0  | 750  | 0.8015          | 0.5867   |
| 0.8478        | 11.0  | 825  | 0.8424          | 0.5533   |
| 0.7471        | 12.0  | 900  | 0.8480          | 0.5733   |
| 0.7041        | 13.0  | 975  | 0.8701          | 0.55     |
| 0.7689        | 14.0  | 1050 | 0.7602          | 0.625    |
| 0.6385        | 15.0  | 1125 | 0.8263          | 0.5933   |
| 0.7131        | 16.0  | 1200 | 0.7809          | 0.595    |
| 0.7152        | 17.0  | 1275 | 0.8940          | 0.565    |
| 0.7023        | 18.0  | 1350 | 0.7651          | 0.66     |
| 0.6514        | 19.0  | 1425 | 0.7331          | 0.6783   |
| 0.7116        | 20.0  | 1500 | 0.7305          | 0.6883   |
| 0.6713        | 21.0  | 1575 | 0.7155          | 0.6733   |
| 0.634         | 22.0  | 1650 | 0.7520          | 0.6883   |
| 0.664         | 23.0  | 1725 | 0.7448          | 0.6767   |
| 0.5579        | 24.0  | 1800 | 0.7383          | 0.6967   |
| 0.6505        | 25.0  | 1875 | 0.7438          | 0.69     |
| 0.6223        | 26.0  | 1950 | 0.7719          | 0.65     |
| 0.5322        | 27.0  | 2025 | 0.7151          | 0.7017   |
| 0.5674        | 28.0  | 2100 | 0.7078          | 0.6817   |
| 0.493         | 29.0  | 2175 | 0.7341          | 0.71     |
| 0.585         | 30.0  | 2250 | 0.7150          | 0.6867   |
| 0.534         | 31.0  | 2325 | 0.7507          | 0.6967   |
| 0.458         | 32.0  | 2400 | 0.7455          | 0.6983   |
| 0.512         | 33.0  | 2475 | 0.6902          | 0.6967   |
| 0.5074        | 34.0  | 2550 | 0.6773          | 0.6983   |
| 0.512         | 35.0  | 2625 | 0.6981          | 0.7083   |
| 0.452         | 36.0  | 2700 | 0.7620          | 0.7083   |
| 0.4013        | 37.0  | 2775 | 0.7597          | 0.7033   |
| 0.4319        | 38.0  | 2850 | 0.7472          | 0.705    |
| 0.4551        | 39.0  | 2925 | 0.8012          | 0.7067   |
| 0.4136        | 40.0  | 3000 | 0.7673          | 0.7133   |
| 0.4092        | 41.0  | 3075 | 0.8184          | 0.7067   |
| 0.412         | 42.0  | 3150 | 0.8145          | 0.7183   |
| 0.4199        | 43.0  | 3225 | 0.8148          | 0.725    |
| 0.3632        | 44.0  | 3300 | 0.8661          | 0.69     |
| 0.2849        | 45.0  | 3375 | 0.9491          | 0.7167   |
| 0.3044        | 46.0  | 3450 | 0.9227          | 0.7017   |
| 0.2713        | 47.0  | 3525 | 0.9951          | 0.6983   |
| 0.22          | 48.0  | 3600 | 1.0641          | 0.7017   |
| 0.2276        | 49.0  | 3675 | 1.1632          | 0.6983   |
| 0.2183        | 50.0  | 3750 | 1.1553          | 0.7017   |


### 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