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# smids_5x_deit_base_sgd_001_fold2

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: 0.3080
- Accuracy: 0.8835

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.848         | 1.0   | 375   | 0.8270          | 0.6972   |
| 0.5847        | 2.0   | 750   | 0.5900          | 0.7787   |
| 0.4307        | 3.0   | 1125  | 0.4834          | 0.8087   |
| 0.3555        | 4.0   | 1500  | 0.4305          | 0.8170   |
| 0.3374        | 5.0   | 1875  | 0.3988          | 0.8386   |
| 0.2952        | 6.0   | 2250  | 0.3778          | 0.8419   |
| 0.2869        | 7.0   | 2625  | 0.3638          | 0.8469   |
| 0.3216        | 8.0   | 3000  | 0.3556          | 0.8519   |
| 0.2811        | 9.0   | 3375  | 0.3457          | 0.8502   |
| 0.2716        | 10.0  | 3750  | 0.3402          | 0.8536   |
| 0.2527        | 11.0  | 4125  | 0.3330          | 0.8586   |
| 0.2868        | 12.0  | 4500  | 0.3315          | 0.8586   |
| 0.2215        | 13.0  | 4875  | 0.3264          | 0.8569   |
| 0.2463        | 14.0  | 5250  | 0.3210          | 0.8652   |
| 0.2529        | 15.0  | 5625  | 0.3196          | 0.8702   |
| 0.2264        | 16.0  | 6000  | 0.3180          | 0.8702   |
| 0.2664        | 17.0  | 6375  | 0.3174          | 0.8686   |
| 0.2072        | 18.0  | 6750  | 0.3154          | 0.8719   |
| 0.2246        | 19.0  | 7125  | 0.3137          | 0.8735   |
| 0.2657        | 20.0  | 7500  | 0.3140          | 0.8785   |
| 0.2076        | 21.0  | 7875  | 0.3137          | 0.8752   |
| 0.2093        | 22.0  | 8250  | 0.3110          | 0.8752   |
| 0.2136        | 23.0  | 8625  | 0.3099          | 0.8802   |
| 0.1894        | 24.0  | 9000  | 0.3103          | 0.8752   |
| 0.1535        | 25.0  | 9375  | 0.3079          | 0.8802   |
| 0.2471        | 26.0  | 9750  | 0.3091          | 0.8785   |
| 0.1996        | 27.0  | 10125 | 0.3086          | 0.8785   |
| 0.1679        | 28.0  | 10500 | 0.3082          | 0.8802   |
| 0.2106        | 29.0  | 10875 | 0.3091          | 0.8819   |
| 0.1991        | 30.0  | 11250 | 0.3064          | 0.8852   |
| 0.2072        | 31.0  | 11625 | 0.3086          | 0.8869   |
| 0.1837        | 32.0  | 12000 | 0.3065          | 0.8852   |
| 0.1829        | 33.0  | 12375 | 0.3083          | 0.8869   |
| 0.146         | 34.0  | 12750 | 0.3077          | 0.8852   |
| 0.2024        | 35.0  | 13125 | 0.3064          | 0.8885   |
| 0.1685        | 36.0  | 13500 | 0.3081          | 0.8852   |
| 0.1849        | 37.0  | 13875 | 0.3080          | 0.8852   |
| 0.1672        | 38.0  | 14250 | 0.3074          | 0.8835   |
| 0.1673        | 39.0  | 14625 | 0.3082          | 0.8835   |
| 0.1726        | 40.0  | 15000 | 0.3082          | 0.8852   |
| 0.1673        | 41.0  | 15375 | 0.3089          | 0.8835   |
| 0.177         | 42.0  | 15750 | 0.3082          | 0.8835   |
| 0.1916        | 43.0  | 16125 | 0.3076          | 0.8835   |
| 0.1782        | 44.0  | 16500 | 0.3080          | 0.8835   |
| 0.1543        | 45.0  | 16875 | 0.3089          | 0.8835   |
| 0.2141        | 46.0  | 17250 | 0.3081          | 0.8835   |
| 0.1912        | 47.0  | 17625 | 0.3084          | 0.8835   |
| 0.1718        | 48.0  | 18000 | 0.3084          | 0.8835   |
| 0.1897        | 49.0  | 18375 | 0.3080          | 0.8835   |
| 0.1329        | 50.0  | 18750 | 0.3080          | 0.8835   |


### 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