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# smids_1x_deit_tiny_rms_001_fold4

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: 0.9960
- Accuracy: 0.7333

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1598        | 1.0   | 75   | 4.0739          | 0.3033   |
| 1.1025        | 2.0   | 150  | 1.1472          | 0.335    |
| 1.0056        | 3.0   | 225  | 0.8843          | 0.5617   |
| 0.9034        | 4.0   | 300  | 0.8985          | 0.5117   |
| 0.8764        | 5.0   | 375  | 0.9613          | 0.5017   |
| 0.9617        | 6.0   | 450  | 0.9074          | 0.5317   |
| 0.8578        | 7.0   | 525  | 0.8240          | 0.5717   |
| 0.8424        | 8.0   | 600  | 0.8437          | 0.5617   |
| 0.8025        | 9.0   | 675  | 0.7942          | 0.5833   |
| 0.7777        | 10.0  | 750  | 0.7683          | 0.57     |
| 0.8053        | 11.0  | 825  | 0.7474          | 0.5983   |
| 0.818         | 12.0  | 900  | 0.7555          | 0.61     |
| 0.8018        | 13.0  | 975  | 0.7629          | 0.5833   |
| 0.8411        | 14.0  | 1050 | 0.7216          | 0.635    |
| 0.6416        | 15.0  | 1125 | 0.8742          | 0.56     |
| 0.8084        | 16.0  | 1200 | 0.7814          | 0.6083   |
| 0.7505        | 17.0  | 1275 | 0.7600          | 0.6183   |
| 0.6996        | 18.0  | 1350 | 0.7346          | 0.6283   |
| 0.7648        | 19.0  | 1425 | 0.7240          | 0.6617   |
| 0.6916        | 20.0  | 1500 | 0.6768          | 0.6767   |
| 0.7556        | 21.0  | 1575 | 0.7263          | 0.6617   |
| 0.6471        | 22.0  | 1650 | 0.7297          | 0.6583   |
| 0.752         | 23.0  | 1725 | 0.7501          | 0.635    |
| 0.7349        | 24.0  | 1800 | 0.6751          | 0.6883   |
| 0.6802        | 25.0  | 1875 | 0.6689          | 0.6817   |
| 0.6239        | 26.0  | 1950 | 0.8871          | 0.5817   |
| 0.6865        | 27.0  | 2025 | 0.6485          | 0.7033   |
| 0.6138        | 28.0  | 2100 | 0.6457          | 0.7233   |
| 0.6707        | 29.0  | 2175 | 0.6937          | 0.6833   |
| 0.6824        | 30.0  | 2250 | 0.6688          | 0.7033   |
| 0.5913        | 31.0  | 2325 | 0.6725          | 0.715    |
| 0.5797        | 32.0  | 2400 | 0.6508          | 0.7167   |
| 0.5524        | 33.0  | 2475 | 0.7048          | 0.7      |
| 0.4736        | 34.0  | 2550 | 0.6807          | 0.6933   |
| 0.5263        | 35.0  | 2625 | 0.6317          | 0.7233   |
| 0.5348        | 36.0  | 2700 | 0.6398          | 0.7367   |
| 0.5082        | 37.0  | 2775 | 0.6440          | 0.7183   |
| 0.4972        | 38.0  | 2850 | 0.6697          | 0.7167   |
| 0.4567        | 39.0  | 2925 | 0.6947          | 0.73     |
| 0.4313        | 40.0  | 3000 | 0.6527          | 0.7383   |
| 0.4762        | 41.0  | 3075 | 0.6875          | 0.74     |
| 0.4293        | 42.0  | 3150 | 0.7259          | 0.7333   |
| 0.4594        | 43.0  | 3225 | 0.7531          | 0.7367   |
| 0.379         | 44.0  | 3300 | 0.7792          | 0.7383   |
| 0.3265        | 45.0  | 3375 | 0.7882          | 0.74     |
| 0.2807        | 46.0  | 3450 | 0.8615          | 0.7367   |
| 0.2733        | 47.0  | 3525 | 0.9438          | 0.73     |
| 0.2273        | 48.0  | 3600 | 0.9312          | 0.73     |
| 0.2189        | 49.0  | 3675 | 0.9889          | 0.7383   |
| 0.1609        | 50.0  | 3750 | 0.9960          | 0.7333   |


### 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