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# smids_5x_deit_base_rms_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: 0.5070
- Accuracy: 0.8067

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0986        | 1.0   | 375   | 1.0985          | 0.3517   |
| 0.9277        | 2.0   | 750   | 0.8963          | 0.5367   |
| 0.8868        | 3.0   | 1125  | 0.8248          | 0.5567   |
| 0.8432        | 4.0   | 1500  | 0.8168          | 0.5367   |
| 0.7756        | 5.0   | 1875  | 0.8234          | 0.555    |
| 0.7692        | 6.0   | 2250  | 0.7291          | 0.6617   |
| 0.7096        | 7.0   | 2625  | 0.7367          | 0.6533   |
| 0.8553        | 8.0   | 3000  | 0.7548          | 0.63     |
| 0.7339        | 9.0   | 3375  | 0.7547          | 0.6233   |
| 0.6755        | 10.0  | 3750  | 0.7150          | 0.665    |
| 0.7434        | 11.0  | 4125  | 0.7034          | 0.685    |
| 0.6966        | 12.0  | 4500  | 0.6998          | 0.6833   |
| 0.6638        | 13.0  | 4875  | 0.8188          | 0.615    |
| 0.7005        | 14.0  | 5250  | 0.6380          | 0.7233   |
| 0.7307        | 15.0  | 5625  | 0.6467          | 0.7017   |
| 0.6252        | 16.0  | 6000  | 0.6189          | 0.7317   |
| 0.6235        | 17.0  | 6375  | 0.5966          | 0.7267   |
| 0.6067        | 18.0  | 6750  | 0.5889          | 0.7367   |
| 0.6586        | 19.0  | 7125  | 0.5888          | 0.745    |
| 0.553         | 20.0  | 7500  | 0.5461          | 0.7583   |
| 0.5457        | 21.0  | 7875  | 0.5458          | 0.7717   |
| 0.535         | 22.0  | 8250  | 0.5661          | 0.745    |
| 0.5802        | 23.0  | 8625  | 0.5673          | 0.7633   |
| 0.585         | 24.0  | 9000  | 0.5456          | 0.7767   |
| 0.5034        | 25.0  | 9375  | 0.5600          | 0.7517   |
| 0.519         | 26.0  | 9750  | 0.5101          | 0.7767   |
| 0.578         | 27.0  | 10125 | 0.5562          | 0.7517   |
| 0.5681        | 28.0  | 10500 | 0.5592          | 0.7633   |
| 0.5613        | 29.0  | 10875 | 0.5207          | 0.7733   |
| 0.4923        | 30.0  | 11250 | 0.5540          | 0.7683   |
| 0.4514        | 31.0  | 11625 | 0.5170          | 0.795    |
| 0.4948        | 32.0  | 12000 | 0.5569          | 0.775    |
| 0.4729        | 33.0  | 12375 | 0.5006          | 0.7967   |
| 0.4583        | 34.0  | 12750 | 0.5008          | 0.7917   |
| 0.4376        | 35.0  | 13125 | 0.4986          | 0.815    |
| 0.3894        | 36.0  | 13500 | 0.5048          | 0.8033   |
| 0.4227        | 37.0  | 13875 | 0.5449          | 0.7883   |
| 0.4237        | 38.0  | 14250 | 0.4850          | 0.81     |
| 0.3609        | 39.0  | 14625 | 0.4881          | 0.8017   |
| 0.4451        | 40.0  | 15000 | 0.5131          | 0.8067   |
| 0.411         | 41.0  | 15375 | 0.5305          | 0.7983   |
| 0.4629        | 42.0  | 15750 | 0.4959          | 0.8      |
| 0.4034        | 43.0  | 16125 | 0.5125          | 0.8083   |
| 0.3681        | 44.0  | 16500 | 0.5034          | 0.8033   |
| 0.4332        | 45.0  | 16875 | 0.4946          | 0.8017   |
| 0.3808        | 46.0  | 17250 | 0.4987          | 0.8067   |
| 0.3828        | 47.0  | 17625 | 0.5113          | 0.8183   |
| 0.2902        | 48.0  | 18000 | 0.5081          | 0.8      |
| 0.3255        | 49.0  | 18375 | 0.5035          | 0.8083   |
| 0.3922        | 50.0  | 18750 | 0.5070          | 0.8067   |


### 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