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# smids_5x_deit_small_rms_00001_fold4

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: 1.4091
- Accuracy: 0.8733

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2357        | 1.0   | 375   | 0.3537          | 0.8617   |
| 0.1744        | 2.0   | 750   | 0.3961          | 0.8667   |
| 0.086         | 3.0   | 1125  | 0.5528          | 0.8517   |
| 0.0758        | 4.0   | 1500  | 0.6078          | 0.87     |
| 0.0324        | 5.0   | 1875  | 0.7790          | 0.8683   |
| 0.0181        | 6.0   | 2250  | 0.9636          | 0.855    |
| 0.0003        | 7.0   | 2625  | 0.8894          | 0.8633   |
| 0.0164        | 8.0   | 3000  | 1.0314          | 0.865    |
| 0.0386        | 9.0   | 3375  | 1.0455          | 0.87     |
| 0.0021        | 10.0  | 3750  | 1.0868          | 0.86     |
| 0.0004        | 11.0  | 4125  | 1.1036          | 0.8667   |
| 0.0001        | 12.0  | 4500  | 1.0921          | 0.8683   |
| 0.0451        | 13.0  | 4875  | 1.1416          | 0.865    |
| 0.0313        | 14.0  | 5250  | 1.1480          | 0.875    |
| 0.0           | 15.0  | 5625  | 1.2563          | 0.8617   |
| 0.0294        | 16.0  | 6000  | 1.1827          | 0.8683   |
| 0.0014        | 17.0  | 6375  | 1.1784          | 0.8683   |
| 0.0017        | 18.0  | 6750  | 1.2479          | 0.8633   |
| 0.0           | 19.0  | 7125  | 1.1339          | 0.8783   |
| 0.0           | 20.0  | 7500  | 1.1679          | 0.8783   |
| 0.0           | 21.0  | 7875  | 1.2344          | 0.8683   |
| 0.0027        | 22.0  | 8250  | 1.2529          | 0.8667   |
| 0.0001        | 23.0  | 8625  | 1.2449          | 0.865    |
| 0.0           | 24.0  | 9000  | 1.2744          | 0.865    |
| 0.0177        | 25.0  | 9375  | 1.2614          | 0.8717   |
| 0.0           | 26.0  | 9750  | 1.2469          | 0.8717   |
| 0.0           | 27.0  | 10125 | 1.3072          | 0.8667   |
| 0.0           | 28.0  | 10500 | 1.2917          | 0.8683   |
| 0.0           | 29.0  | 10875 | 1.2871          | 0.8767   |
| 0.0           | 30.0  | 11250 | 1.3156          | 0.8617   |
| 0.0           | 31.0  | 11625 | 1.2509          | 0.8767   |
| 0.0           | 32.0  | 12000 | 1.2613          | 0.8783   |
| 0.0           | 33.0  | 12375 | 1.3454          | 0.8683   |
| 0.0001        | 34.0  | 12750 | 1.2978          | 0.8667   |
| 0.0           | 35.0  | 13125 | 1.2980          | 0.8617   |
| 0.0           | 36.0  | 13500 | 1.2859          | 0.8683   |
| 0.0           | 37.0  | 13875 | 1.3447          | 0.8683   |
| 0.0044        | 38.0  | 14250 | 1.3496          | 0.87     |
| 0.0           | 39.0  | 14625 | 1.3716          | 0.8717   |
| 0.0           | 40.0  | 15000 | 1.3540          | 0.8733   |
| 0.0           | 41.0  | 15375 | 1.3616          | 0.8733   |
| 0.0           | 42.0  | 15750 | 1.3855          | 0.8717   |
| 0.0           | 43.0  | 16125 | 1.3813          | 0.8733   |
| 0.0           | 44.0  | 16500 | 1.4013          | 0.8717   |
| 0.0           | 45.0  | 16875 | 1.3939          | 0.875    |
| 0.0           | 46.0  | 17250 | 1.3982          | 0.875    |
| 0.0           | 47.0  | 17625 | 1.4014          | 0.875    |
| 0.0           | 48.0  | 18000 | 1.4060          | 0.8733   |
| 0.0           | 49.0  | 18375 | 1.4084          | 0.8733   |
| 0.0           | 50.0  | 18750 | 1.4091          | 0.8733   |


### 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