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# smids_10x_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: 0.9349
- Accuracy: 0.5591

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0693        | 1.0   | 750   | 1.0692          | 0.4293   |
| 1.0584        | 2.0   | 1500  | 1.0648          | 0.4343   |
| 1.0342        | 3.0   | 2250  | 1.0600          | 0.4376   |
| 1.0374        | 4.0   | 3000  | 1.0551          | 0.4443   |
| 1.028         | 5.0   | 3750  | 1.0500          | 0.4459   |
| 1.0131        | 6.0   | 4500  | 1.0451          | 0.4476   |
| 1.022         | 7.0   | 5250  | 1.0402          | 0.4459   |
| 1.0192        | 8.0   | 6000  | 1.0354          | 0.4526   |
| 1.0168        | 9.0   | 6750  | 1.0306          | 0.4576   |
| 0.9985        | 10.0  | 7500  | 1.0259          | 0.4592   |
| 0.9898        | 11.0  | 8250  | 1.0213          | 0.4609   |
| 1.0116        | 12.0  | 9000  | 1.0168          | 0.4642   |
| 0.9986        | 13.0  | 9750  | 1.0125          | 0.4659   |
| 0.9818        | 14.0  | 10500 | 1.0083          | 0.4759   |
| 0.9837        | 15.0  | 11250 | 1.0041          | 0.4809   |
| 0.9601        | 16.0  | 12000 | 1.0001          | 0.4809   |
| 0.9572        | 17.0  | 12750 | 0.9961          | 0.4809   |
| 0.9406        | 18.0  | 13500 | 0.9923          | 0.4859   |
| 0.9621        | 19.0  | 14250 | 0.9887          | 0.4892   |
| 0.9467        | 20.0  | 15000 | 0.9850          | 0.4925   |
| 0.9691        | 21.0  | 15750 | 0.9816          | 0.4992   |
| 0.9406        | 22.0  | 16500 | 0.9782          | 0.5008   |
| 0.9223        | 23.0  | 17250 | 0.9750          | 0.5058   |
| 0.9127        | 24.0  | 18000 | 0.9718          | 0.5075   |
| 0.9371        | 25.0  | 18750 | 0.9688          | 0.5141   |
| 0.9589        | 26.0  | 19500 | 0.9659          | 0.5175   |
| 0.9189        | 27.0  | 20250 | 0.9631          | 0.5208   |
| 0.9249        | 28.0  | 21000 | 0.9605          | 0.5258   |
| 0.927         | 29.0  | 21750 | 0.9580          | 0.5275   |
| 0.9378        | 30.0  | 22500 | 0.9556          | 0.5308   |
| 0.8829        | 31.0  | 23250 | 0.9533          | 0.5308   |
| 0.931         | 32.0  | 24000 | 0.9512          | 0.5341   |
| 0.9197        | 33.0  | 24750 | 0.9492          | 0.5374   |
| 0.9032        | 34.0  | 25500 | 0.9474          | 0.5374   |
| 0.9           | 35.0  | 26250 | 0.9457          | 0.5391   |
| 0.8939        | 36.0  | 27000 | 0.9442          | 0.5441   |
| 0.9276        | 37.0  | 27750 | 0.9427          | 0.5458   |
| 0.8712        | 38.0  | 28500 | 0.9414          | 0.5458   |
| 0.9222        | 39.0  | 29250 | 0.9402          | 0.5458   |
| 0.8913        | 40.0  | 30000 | 0.9392          | 0.5474   |
| 0.8879        | 41.0  | 30750 | 0.9383          | 0.5474   |
| 0.8851        | 42.0  | 31500 | 0.9375          | 0.5541   |
| 0.8777        | 43.0  | 32250 | 0.9368          | 0.5541   |
| 0.8945        | 44.0  | 33000 | 0.9362          | 0.5541   |
| 0.8708        | 45.0  | 33750 | 0.9358          | 0.5574   |
| 0.9082        | 46.0  | 34500 | 0.9354          | 0.5591   |
| 0.9028        | 47.0  | 35250 | 0.9352          | 0.5591   |
| 0.8903        | 48.0  | 36000 | 0.9350          | 0.5591   |
| 0.8994        | 49.0  | 36750 | 0.9349          | 0.5591   |
| 0.9183        | 50.0  | 37500 | 0.9349          | 0.5591   |


### 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