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# smids_1x_deit_tiny_rms_001_fold3

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: 1.5663
- Accuracy: 0.7317

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1316        | 1.0   | 75   | 1.0924          | 0.485    |
| 0.926         | 2.0   | 150  | 0.9324          | 0.4883   |
| 0.9183        | 3.0   | 225  | 0.9377          | 0.5067   |
| 0.8817        | 4.0   | 300  | 0.8909          | 0.5233   |
| 0.9151        | 5.0   | 375  | 0.8809          | 0.5467   |
| 0.8442        | 6.0   | 450  | 0.8675          | 0.5433   |
| 0.8079        | 7.0   | 525  | 0.8557          | 0.56     |
| 0.7894        | 8.0   | 600  | 0.8225          | 0.56     |
| 0.8216        | 9.0   | 675  | 0.7829          | 0.5967   |
| 0.8087        | 10.0  | 750  | 0.8146          | 0.6033   |
| 0.748         | 11.0  | 825  | 1.0004          | 0.5133   |
| 0.7666        | 12.0  | 900  | 0.8100          | 0.62     |
| 0.6897        | 13.0  | 975  | 0.7991          | 0.5483   |
| 0.7137        | 14.0  | 1050 | 0.8839          | 0.6233   |
| 0.5912        | 15.0  | 1125 | 0.8794          | 0.5883   |
| 0.6325        | 16.0  | 1200 | 0.7543          | 0.6117   |
| 0.6753        | 17.0  | 1275 | 0.6833          | 0.6983   |
| 0.6515        | 18.0  | 1350 | 0.7976          | 0.6667   |
| 0.646         | 19.0  | 1425 | 0.7002          | 0.6983   |
| 0.6399        | 20.0  | 1500 | 0.6939          | 0.6783   |
| 0.6505        | 21.0  | 1575 | 0.7405          | 0.68     |
| 0.576         | 22.0  | 1650 | 0.6489          | 0.7283   |
| 0.6111        | 23.0  | 1725 | 0.7398          | 0.685    |
| 0.5464        | 24.0  | 1800 | 0.6157          | 0.7283   |
| 0.5394        | 25.0  | 1875 | 0.6999          | 0.6983   |
| 0.5477        | 26.0  | 1950 | 0.8201          | 0.6383   |
| 0.4763        | 27.0  | 2025 | 0.6302          | 0.725    |
| 0.4373        | 28.0  | 2100 | 0.6307          | 0.7217   |
| 0.5016        | 29.0  | 2175 | 0.6401          | 0.7283   |
| 0.4888        | 30.0  | 2250 | 0.6903          | 0.73     |
| 0.4977        | 31.0  | 2325 | 0.6449          | 0.7233   |
| 0.4672        | 32.0  | 2400 | 0.6248          | 0.735    |
| 0.378         | 33.0  | 2475 | 0.7223          | 0.7133   |
| 0.4146        | 34.0  | 2550 | 0.6409          | 0.73     |
| 0.3898        | 35.0  | 2625 | 0.6376          | 0.7517   |
| 0.3685        | 36.0  | 2700 | 0.6638          | 0.75     |
| 0.3996        | 37.0  | 2775 | 0.6101          | 0.7633   |
| 0.3384        | 38.0  | 2850 | 0.7105          | 0.745    |
| 0.3173        | 39.0  | 2925 | 0.6903          | 0.75     |
| 0.2666        | 40.0  | 3000 | 0.7489          | 0.7383   |
| 0.217         | 41.0  | 3075 | 0.7934          | 0.7267   |
| 0.2624        | 42.0  | 3150 | 0.8366          | 0.73     |
| 0.2541        | 43.0  | 3225 | 0.9217          | 0.7317   |
| 0.1857        | 44.0  | 3300 | 0.9118          | 0.7333   |
| 0.1064        | 45.0  | 3375 | 1.0537          | 0.7383   |
| 0.1268        | 46.0  | 3450 | 1.1266          | 0.7317   |
| 0.092         | 47.0  | 3525 | 1.2383          | 0.7433   |
| 0.0334        | 48.0  | 3600 | 1.4560          | 0.74     |
| 0.0452        | 49.0  | 3675 | 1.5075          | 0.7367   |
| 0.0233        | 50.0  | 3750 | 1.5663          | 0.7317   |


### 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