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# smids_5x_deit_small_sgd_00001_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: 0.9907
- Accuracy: 0.5108

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0998        | 1.0   | 375   | 1.0701          | 0.4260   |
| 1.0872        | 2.0   | 750   | 1.0668          | 0.4276   |
| 1.0721        | 3.0   | 1125  | 1.0636          | 0.4343   |
| 1.068         | 4.0   | 1500  | 1.0604          | 0.4343   |
| 1.0595        | 5.0   | 1875  | 1.0573          | 0.4376   |
| 1.0368        | 6.0   | 2250  | 1.0542          | 0.4409   |
| 1.0324        | 7.0   | 2625  | 1.0513          | 0.4493   |
| 1.0454        | 8.0   | 3000  | 1.0484          | 0.4526   |
| 1.0424        | 9.0   | 3375  | 1.0456          | 0.4509   |
| 1.0256        | 10.0  | 3750  | 1.0428          | 0.4559   |
| 1.0778        | 11.0  | 4125  | 1.0401          | 0.4559   |
| 1.0323        | 12.0  | 4500  | 1.0375          | 0.4592   |
| 1.0063        | 13.0  | 4875  | 1.0349          | 0.4626   |
| 1.0266        | 14.0  | 5250  | 1.0324          | 0.4642   |
| 1.0153        | 15.0  | 5625  | 1.0299          | 0.4659   |
| 1.0           | 16.0  | 6000  | 1.0276          | 0.4692   |
| 1.0251        | 17.0  | 6375  | 1.0253          | 0.4692   |
| 1.0305        | 18.0  | 6750  | 1.0231          | 0.4725   |
| 1.0097        | 19.0  | 7125  | 1.0209          | 0.4725   |
| 1.02          | 20.0  | 7500  | 1.0189          | 0.4725   |
| 0.9981        | 21.0  | 7875  | 1.0168          | 0.4775   |
| 0.9952        | 22.0  | 8250  | 1.0149          | 0.4775   |
| 1.007         | 23.0  | 8625  | 1.0131          | 0.4859   |
| 1.0141        | 24.0  | 9000  | 1.0113          | 0.4875   |
| 1.0041        | 25.0  | 9375  | 1.0095          | 0.4875   |
| 1.0032        | 26.0  | 9750  | 1.0079          | 0.4875   |
| 1.0112        | 27.0  | 10125 | 1.0064          | 0.4875   |
| 0.9862        | 28.0  | 10500 | 1.0049          | 0.4892   |
| 0.9745        | 29.0  | 10875 | 1.0035          | 0.4892   |
| 0.9881        | 30.0  | 11250 | 1.0022          | 0.4925   |
| 0.9936        | 31.0  | 11625 | 1.0009          | 0.4908   |
| 0.9959        | 32.0  | 12000 | 0.9998          | 0.4892   |
| 0.9804        | 33.0  | 12375 | 0.9987          | 0.4875   |
| 0.9795        | 34.0  | 12750 | 0.9977          | 0.4958   |
| 0.9724        | 35.0  | 13125 | 0.9967          | 0.4975   |
| 0.9957        | 36.0  | 13500 | 0.9958          | 0.4992   |
| 0.9651        | 37.0  | 13875 | 0.9950          | 0.5025   |
| 0.9869        | 38.0  | 14250 | 0.9943          | 0.5042   |
| 0.9664        | 39.0  | 14625 | 0.9937          | 0.5075   |
| 0.9769        | 40.0  | 15000 | 0.9931          | 0.5092   |
| 0.9473        | 41.0  | 15375 | 0.9926          | 0.5108   |
| 0.9911        | 42.0  | 15750 | 0.9921          | 0.5108   |
| 0.9625        | 43.0  | 16125 | 0.9917          | 0.5108   |
| 0.9689        | 44.0  | 16500 | 0.9914          | 0.5108   |
| 0.9736        | 45.0  | 16875 | 0.9912          | 0.5108   |
| 0.9789        | 46.0  | 17250 | 0.9910          | 0.5108   |
| 0.9732        | 47.0  | 17625 | 0.9908          | 0.5108   |
| 0.9789        | 48.0  | 18000 | 0.9907          | 0.5108   |
| 0.987         | 49.0  | 18375 | 0.9907          | 0.5108   |
| 1.0128        | 50.0  | 18750 | 0.9907          | 0.5108   |


### 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