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# smids_5x_deit_base_adamax_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: 0.8666
- Accuracy: 0.9067

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3384        | 1.0   | 375   | 0.4257          | 0.8267   |
| 0.3243        | 2.0   | 750   | 0.3051          | 0.8883   |
| 0.2315        | 3.0   | 1125  | 0.3393          | 0.8783   |
| 0.1699        | 4.0   | 1500  | 0.4297          | 0.8583   |
| 0.1105        | 5.0   | 1875  | 0.3821          | 0.8983   |
| 0.1049        | 6.0   | 2250  | 0.3824          | 0.895    |
| 0.0625        | 7.0   | 2625  | 0.5340          | 0.8967   |
| 0.0706        | 8.0   | 3000  | 0.5827          | 0.8783   |
| 0.039         | 9.0   | 3375  | 0.4159          | 0.895    |
| 0.0887        | 10.0  | 3750  | 0.4518          | 0.905    |
| 0.042         | 11.0  | 4125  | 0.4385          | 0.91     |
| 0.0677        | 12.0  | 4500  | 0.5266          | 0.8983   |
| 0.0355        | 13.0  | 4875  | 0.4982          | 0.8883   |
| 0.0188        | 14.0  | 5250  | 0.5825          | 0.9083   |
| 0.0091        | 15.0  | 5625  | 0.4685          | 0.915    |
| 0.0008        | 16.0  | 6000  | 0.6661          | 0.8983   |
| 0.026         | 17.0  | 6375  | 0.5630          | 0.9      |
| 0.0121        | 18.0  | 6750  | 0.6999          | 0.8967   |
| 0.0069        | 19.0  | 7125  | 0.5495          | 0.9083   |
| 0.0011        | 20.0  | 7500  | 0.6260          | 0.9033   |
| 0.0026        | 21.0  | 7875  | 0.6616          | 0.91     |
| 0.0056        | 22.0  | 8250  | 0.6236          | 0.915    |
| 0.0072        | 23.0  | 8625  | 0.7060          | 0.905    |
| 0.0005        | 24.0  | 9000  | 0.7311          | 0.9067   |
| 0.0           | 25.0  | 9375  | 0.7450          | 0.91     |
| 0.0001        | 26.0  | 9750  | 0.7238          | 0.91     |
| 0.0019        | 27.0  | 10125 | 0.7673          | 0.9      |
| 0.0007        | 28.0  | 10500 | 0.7394          | 0.91     |
| 0.0072        | 29.0  | 10875 | 0.7457          | 0.91     |
| 0.0069        | 30.0  | 11250 | 0.9604          | 0.8883   |
| 0.0           | 31.0  | 11625 | 0.7446          | 0.91     |
| 0.0           | 32.0  | 12000 | 0.7855          | 0.905    |
| 0.0           | 33.0  | 12375 | 0.7691          | 0.905    |
| 0.0           | 34.0  | 12750 | 0.7719          | 0.9067   |
| 0.0           | 35.0  | 13125 | 0.7976          | 0.9017   |
| 0.0           | 36.0  | 13500 | 0.8067          | 0.9033   |
| 0.0           | 37.0  | 13875 | 0.7973          | 0.9067   |
| 0.0041        | 38.0  | 14250 | 0.8120          | 0.9067   |
| 0.0           | 39.0  | 14625 | 0.8149          | 0.9067   |
| 0.0           | 40.0  | 15000 | 0.7879          | 0.9067   |
| 0.0           | 41.0  | 15375 | 0.8013          | 0.9067   |
| 0.0           | 42.0  | 15750 | 0.8079          | 0.905    |
| 0.0           | 43.0  | 16125 | 0.8212          | 0.9017   |
| 0.0           | 44.0  | 16500 | 0.8180          | 0.905    |
| 0.0           | 45.0  | 16875 | 0.8381          | 0.9067   |
| 0.0           | 46.0  | 17250 | 0.8519          | 0.905    |
| 0.003         | 47.0  | 17625 | 0.8539          | 0.9067   |
| 0.0           | 48.0  | 18000 | 0.8604          | 0.9083   |
| 0.0           | 49.0  | 18375 | 0.8650          | 0.9067   |
| 0.0021        | 50.0  | 18750 | 0.8666          | 0.9067   |


### 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