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# smids_5x_deit_tiny_sgd_00001_fold4

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0682
- Accuracy: 0.4133

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3789        | 1.0   | 375   | 1.3304          | 0.3467   |
| 1.3522        | 2.0   | 750   | 1.2980          | 0.3417   |
| 1.2851        | 3.0   | 1125  | 1.2700          | 0.3467   |
| 1.3268        | 4.0   | 1500  | 1.2462          | 0.35     |
| 1.2083        | 5.0   | 1875  | 1.2264          | 0.3533   |
| 1.2564        | 6.0   | 2250  | 1.2096          | 0.3667   |
| 1.2076        | 7.0   | 2625  | 1.1953          | 0.375    |
| 1.1738        | 8.0   | 3000  | 1.1833          | 0.375    |
| 1.1964        | 9.0   | 3375  | 1.1730          | 0.3767   |
| 1.1824        | 10.0  | 3750  | 1.1642          | 0.375    |
| 1.1746        | 11.0  | 4125  | 1.1567          | 0.375    |
| 1.0941        | 12.0  | 4500  | 1.1499          | 0.3783   |
| 1.1561        | 13.0  | 4875  | 1.1439          | 0.3817   |
| 1.1702        | 14.0  | 5250  | 1.1384          | 0.3817   |
| 1.1181        | 15.0  | 5625  | 1.1334          | 0.3867   |
| 1.149         | 16.0  | 6000  | 1.1288          | 0.3833   |
| 1.1131        | 17.0  | 6375  | 1.1244          | 0.3867   |
| 1.1335        | 18.0  | 6750  | 1.1203          | 0.39     |
| 1.105         | 19.0  | 7125  | 1.1164          | 0.3933   |
| 1.0655        | 20.0  | 7500  | 1.1128          | 0.3933   |
| 1.1098        | 21.0  | 7875  | 1.1094          | 0.395    |
| 1.0972        | 22.0  | 8250  | 1.1061          | 0.3933   |
| 1.112         | 23.0  | 8625  | 1.1030          | 0.3917   |
| 1.0932        | 24.0  | 9000  | 1.1001          | 0.395    |
| 1.0801        | 25.0  | 9375  | 1.0974          | 0.3933   |
| 1.1085        | 26.0  | 9750  | 1.0947          | 0.4      |
| 1.1153        | 27.0  | 10125 | 1.0922          | 0.4      |
| 1.0883        | 28.0  | 10500 | 1.0899          | 0.4      |
| 1.0621        | 29.0  | 10875 | 1.0877          | 0.4017   |
| 1.0559        | 30.0  | 11250 | 1.0856          | 0.4017   |
| 1.0795        | 31.0  | 11625 | 1.0837          | 0.4      |
| 1.1076        | 32.0  | 12000 | 1.0819          | 0.4017   |
| 1.1027        | 33.0  | 12375 | 1.0802          | 0.405    |
| 1.0471        | 34.0  | 12750 | 1.0787          | 0.41     |
| 1.032         | 35.0  | 13125 | 1.0772          | 0.4117   |
| 1.0529        | 36.0  | 13500 | 1.0759          | 0.4083   |
| 1.0365        | 37.0  | 13875 | 1.0747          | 0.4067   |
| 1.0659        | 38.0  | 14250 | 1.0736          | 0.4067   |
| 1.073         | 39.0  | 14625 | 1.0726          | 0.4117   |
| 1.1034        | 40.0  | 15000 | 1.0717          | 0.4117   |
| 1.0918        | 41.0  | 15375 | 1.0710          | 0.4117   |
| 1.0873        | 42.0  | 15750 | 1.0703          | 0.4133   |
| 1.0582        | 43.0  | 16125 | 1.0697          | 0.4133   |
| 1.0527        | 44.0  | 16500 | 1.0693          | 0.4133   |
| 1.0394        | 45.0  | 16875 | 1.0689          | 0.4133   |
| 1.0718        | 46.0  | 17250 | 1.0686          | 0.4133   |
| 1.0719        | 47.0  | 17625 | 1.0684          | 0.4133   |
| 1.0655        | 48.0  | 18000 | 1.0683          | 0.4133   |
| 1.0516        | 49.0  | 18375 | 1.0682          | 0.4133   |
| 1.0396        | 50.0  | 18750 | 1.0682          | 0.4133   |


### 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