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# smids_5x_deit_base_adamax_00001_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: 0.8137
- Accuracy: 0.8885

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2996        | 1.0   | 375   | 0.3210          | 0.8735   |
| 0.2177        | 2.0   | 750   | 0.2936          | 0.8902   |
| 0.1354        | 3.0   | 1125  | 0.3183          | 0.8852   |
| 0.0424        | 4.0   | 1500  | 0.3451          | 0.8902   |
| 0.0243        | 5.0   | 1875  | 0.3990          | 0.8852   |
| 0.0226        | 6.0   | 2250  | 0.4326          | 0.8869   |
| 0.0168        | 7.0   | 2625  | 0.4792          | 0.8985   |
| 0.0076        | 8.0   | 3000  | 0.5341          | 0.8885   |
| 0.0045        | 9.0   | 3375  | 0.5636          | 0.8852   |
| 0.0004        | 10.0  | 3750  | 0.5967          | 0.8902   |
| 0.0098        | 11.0  | 4125  | 0.6469          | 0.8819   |
| 0.0003        | 12.0  | 4500  | 0.6549          | 0.8835   |
| 0.0002        | 13.0  | 4875  | 0.6787          | 0.8819   |
| 0.0001        | 14.0  | 5250  | 0.6859          | 0.8902   |
| 0.0001        | 15.0  | 5625  | 0.6864          | 0.8902   |
| 0.0001        | 16.0  | 6000  | 0.6911          | 0.8852   |
| 0.0065        | 17.0  | 6375  | 0.7430          | 0.8852   |
| 0.0           | 18.0  | 6750  | 0.7047          | 0.8869   |
| 0.0           | 19.0  | 7125  | 0.7306          | 0.8835   |
| 0.0           | 20.0  | 7500  | 0.7458          | 0.8835   |
| 0.0           | 21.0  | 7875  | 0.7324          | 0.8752   |
| 0.0           | 22.0  | 8250  | 0.7468          | 0.8802   |
| 0.0           | 23.0  | 8625  | 0.7452          | 0.8869   |
| 0.0           | 24.0  | 9000  | 0.7743          | 0.8785   |
| 0.0           | 25.0  | 9375  | 0.7579          | 0.8852   |
| 0.0           | 26.0  | 9750  | 0.7668          | 0.8802   |
| 0.0           | 27.0  | 10125 | 0.7673          | 0.8852   |
| 0.0           | 28.0  | 10500 | 0.7789          | 0.8802   |
| 0.0           | 29.0  | 10875 | 0.7704          | 0.8902   |
| 0.0           | 30.0  | 11250 | 0.7833          | 0.8869   |
| 0.003         | 31.0  | 11625 | 0.7716          | 0.8819   |
| 0.0119        | 32.0  | 12000 | 0.7822          | 0.8902   |
| 0.0022        | 33.0  | 12375 | 0.7945          | 0.8869   |
| 0.0           | 34.0  | 12750 | 0.7824          | 0.8902   |
| 0.0           | 35.0  | 13125 | 0.8058          | 0.8835   |
| 0.0           | 36.0  | 13500 | 0.7871          | 0.8835   |
| 0.0           | 37.0  | 13875 | 0.7901          | 0.8852   |
| 0.0           | 38.0  | 14250 | 0.7886          | 0.8885   |
| 0.0           | 39.0  | 14625 | 0.8070          | 0.8885   |
| 0.0023        | 40.0  | 15000 | 0.8050          | 0.8935   |
| 0.0           | 41.0  | 15375 | 0.8034          | 0.8918   |
| 0.003         | 42.0  | 15750 | 0.8038          | 0.8918   |
| 0.0024        | 43.0  | 16125 | 0.8073          | 0.8902   |
| 0.0024        | 44.0  | 16500 | 0.8102          | 0.8869   |
| 0.0028        | 45.0  | 16875 | 0.8121          | 0.8885   |
| 0.0           | 46.0  | 17250 | 0.8095          | 0.8918   |
| 0.0035        | 47.0  | 17625 | 0.8116          | 0.8885   |
| 0.0           | 48.0  | 18000 | 0.8125          | 0.8885   |
| 0.0026        | 49.0  | 18375 | 0.8131          | 0.8885   |
| 0.0022        | 50.0  | 18750 | 0.8137          | 0.8885   |


### 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