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# smids_10x_deit_base_sgd_001_fold5

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: 0.2447
- Accuracy: 0.8967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5962        | 1.0   | 750   | 0.5861          | 0.7883   |
| 0.4051        | 2.0   | 1500  | 0.4173          | 0.8267   |
| 0.3569        | 3.0   | 2250  | 0.3612          | 0.8367   |
| 0.3447        | 4.0   | 3000  | 0.3341          | 0.85     |
| 0.3363        | 5.0   | 3750  | 0.3169          | 0.855    |
| 0.2736        | 6.0   | 4500  | 0.3026          | 0.8683   |
| 0.2339        | 7.0   | 5250  | 0.2954          | 0.87     |
| 0.2686        | 8.0   | 6000  | 0.2855          | 0.8683   |
| 0.2668        | 9.0   | 6750  | 0.2807          | 0.8733   |
| 0.247         | 10.0  | 7500  | 0.2762          | 0.8783   |
| 0.2811        | 11.0  | 8250  | 0.2739          | 0.89     |
| 0.2638        | 12.0  | 9000  | 0.2726          | 0.8833   |
| 0.2445        | 13.0  | 9750  | 0.2668          | 0.8883   |
| 0.245         | 14.0  | 10500 | 0.2627          | 0.8883   |
| 0.2557        | 15.0  | 11250 | 0.2593          | 0.8867   |
| 0.1782        | 16.0  | 12000 | 0.2589          | 0.8867   |
| 0.2171        | 17.0  | 12750 | 0.2586          | 0.8883   |
| 0.1998        | 18.0  | 13500 | 0.2548          | 0.8933   |
| 0.2462        | 19.0  | 14250 | 0.2572          | 0.8917   |
| 0.1609        | 20.0  | 15000 | 0.2549          | 0.8933   |
| 0.1833        | 21.0  | 15750 | 0.2494          | 0.895    |
| 0.2212        | 22.0  | 16500 | 0.2509          | 0.895    |
| 0.2078        | 23.0  | 17250 | 0.2493          | 0.895    |
| 0.1922        | 24.0  | 18000 | 0.2508          | 0.8983   |
| 0.2035        | 25.0  | 18750 | 0.2506          | 0.8933   |
| 0.1816        | 26.0  | 19500 | 0.2465          | 0.8967   |
| 0.1488        | 27.0  | 20250 | 0.2466          | 0.8983   |
| 0.1736        | 28.0  | 21000 | 0.2478          | 0.8967   |
| 0.1851        | 29.0  | 21750 | 0.2450          | 0.8967   |
| 0.2091        | 30.0  | 22500 | 0.2502          | 0.8933   |
| 0.1735        | 31.0  | 23250 | 0.2445          | 0.8983   |
| 0.1511        | 32.0  | 24000 | 0.2473          | 0.895    |
| 0.1917        | 33.0  | 24750 | 0.2450          | 0.895    |
| 0.1536        | 34.0  | 25500 | 0.2464          | 0.8983   |
| 0.1399        | 35.0  | 26250 | 0.2436          | 0.895    |
| 0.1867        | 36.0  | 27000 | 0.2448          | 0.8983   |
| 0.1193        | 37.0  | 27750 | 0.2459          | 0.8967   |
| 0.1456        | 38.0  | 28500 | 0.2448          | 0.9017   |
| 0.1489        | 39.0  | 29250 | 0.2453          | 0.8967   |
| 0.1393        | 40.0  | 30000 | 0.2452          | 0.8983   |
| 0.1841        | 41.0  | 30750 | 0.2456          | 0.8967   |
| 0.1682        | 42.0  | 31500 | 0.2446          | 0.8967   |
| 0.1428        | 43.0  | 32250 | 0.2457          | 0.9      |
| 0.1636        | 44.0  | 33000 | 0.2455          | 0.8967   |
| 0.1783        | 45.0  | 33750 | 0.2453          | 0.8983   |
| 0.1167        | 46.0  | 34500 | 0.2456          | 0.8967   |
| 0.1786        | 47.0  | 35250 | 0.2449          | 0.8967   |
| 0.1666        | 48.0  | 36000 | 0.2447          | 0.8983   |
| 0.1479        | 49.0  | 36750 | 0.2447          | 0.8967   |
| 0.099         | 50.0  | 37500 | 0.2447          | 0.8967   |


### 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