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# smids_5x_deit_tiny_adamax_001_fold2

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8883
- Accuracy: 0.8968

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3644        | 1.0   | 375   | 0.3398          | 0.8702   |
| 0.2716        | 2.0   | 750   | 0.3172          | 0.8735   |
| 0.3497        | 3.0   | 1125  | 0.3400          | 0.8586   |
| 0.1669        | 4.0   | 1500  | 0.3794          | 0.8669   |
| 0.2114        | 5.0   | 1875  | 0.2911          | 0.8902   |
| 0.1067        | 6.0   | 2250  | 0.4133          | 0.8752   |
| 0.1489        | 7.0   | 2625  | 0.5329          | 0.8419   |
| 0.1233        | 8.0   | 3000  | 0.4750          | 0.8769   |
| 0.121         | 9.0   | 3375  | 0.4209          | 0.8852   |
| 0.0613        | 10.0  | 3750  | 0.3960          | 0.8918   |
| 0.0185        | 11.0  | 4125  | 0.5647          | 0.8769   |
| 0.07          | 12.0  | 4500  | 0.5185          | 0.8586   |
| 0.0467        | 13.0  | 4875  | 0.5032          | 0.8985   |
| 0.0041        | 14.0  | 5250  | 0.5742          | 0.8918   |
| 0.0599        | 15.0  | 5625  | 0.7221          | 0.8652   |
| 0.0363        | 16.0  | 6000  | 0.6853          | 0.8852   |
| 0.0212        | 17.0  | 6375  | 0.5687          | 0.8985   |
| 0.0007        | 18.0  | 6750  | 0.6790          | 0.8702   |
| 0.0025        | 19.0  | 7125  | 0.5146          | 0.8935   |
| 0.0511        | 20.0  | 7500  | 0.4949          | 0.9052   |
| 0.0231        | 21.0  | 7875  | 0.5535          | 0.8952   |
| 0.0           | 22.0  | 8250  | 0.7099          | 0.9002   |
| 0.011         | 23.0  | 8625  | 0.7090          | 0.8902   |
| 0.0118        | 24.0  | 9000  | 0.7009          | 0.9068   |
| 0.0           | 25.0  | 9375  | 0.6598          | 0.8985   |
| 0.0089        | 26.0  | 9750  | 0.7133          | 0.8902   |
| 0.0142        | 27.0  | 10125 | 0.5886          | 0.9052   |
| 0.0           | 28.0  | 10500 | 0.6881          | 0.9018   |
| 0.0001        | 29.0  | 10875 | 0.7679          | 0.8985   |
| 0.0001        | 30.0  | 11250 | 0.7339          | 0.8968   |
| 0.0038        | 31.0  | 11625 | 0.8413          | 0.8918   |
| 0.0044        | 32.0  | 12000 | 0.7669          | 0.9035   |
| 0.0049        | 33.0  | 12375 | 0.7980          | 0.9052   |
| 0.0           | 34.0  | 12750 | 0.7835          | 0.9035   |
| 0.0           | 35.0  | 13125 | 0.8137          | 0.8968   |
| 0.0           | 36.0  | 13500 | 0.8434          | 0.8968   |
| 0.0           | 37.0  | 13875 | 0.8282          | 0.8952   |
| 0.0           | 38.0  | 14250 | 0.8297          | 0.8968   |
| 0.0           | 39.0  | 14625 | 0.8386          | 0.8935   |
| 0.0034        | 40.0  | 15000 | 0.8364          | 0.8952   |
| 0.0           | 41.0  | 15375 | 0.8624          | 0.8985   |
| 0.0031        | 42.0  | 15750 | 0.8414          | 0.8968   |
| 0.0026        | 43.0  | 16125 | 0.9010          | 0.8902   |
| 0.0026        | 44.0  | 16500 | 0.8826          | 0.8952   |
| 0.0029        | 45.0  | 16875 | 0.8702          | 0.8968   |
| 0.0           | 46.0  | 17250 | 0.8727          | 0.8968   |
| 0.0055        | 47.0  | 17625 | 0.8804          | 0.8968   |
| 0.0           | 48.0  | 18000 | 0.8849          | 0.8968   |
| 0.0025        | 49.0  | 18375 | 0.8877          | 0.8968   |
| 0.0023        | 50.0  | 18750 | 0.8883          | 0.8968   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm