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# smids_1x_deit_tiny_sgd_001_fold2

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: 0.4027
- Accuracy: 0.8303

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.092         | 1.0   | 75   | 1.0391          | 0.4642   |
| 0.9734        | 2.0   | 150  | 0.9266          | 0.5641   |
| 0.8567        | 3.0   | 225  | 0.8357          | 0.6339   |
| 0.7624        | 4.0   | 300  | 0.7647          | 0.6705   |
| 0.6877        | 5.0   | 375  | 0.7050          | 0.7205   |
| 0.6614        | 6.0   | 450  | 0.6522          | 0.7488   |
| 0.6494        | 7.0   | 525  | 0.6133          | 0.7604   |
| 0.6282        | 8.0   | 600  | 0.5876          | 0.7687   |
| 0.5404        | 9.0   | 675  | 0.5590          | 0.7787   |
| 0.5855        | 10.0  | 750  | 0.5391          | 0.7837   |
| 0.5304        | 11.0  | 825  | 0.5290          | 0.7820   |
| 0.4323        | 12.0  | 900  | 0.5141          | 0.7854   |
| 0.4483        | 13.0  | 975  | 0.4996          | 0.7987   |
| 0.3743        | 14.0  | 1050 | 0.4908          | 0.7970   |
| 0.3865        | 15.0  | 1125 | 0.4830          | 0.8003   |
| 0.4258        | 16.0  | 1200 | 0.4748          | 0.8053   |
| 0.3735        | 17.0  | 1275 | 0.4651          | 0.8087   |
| 0.4081        | 18.0  | 1350 | 0.4581          | 0.8087   |
| 0.4212        | 19.0  | 1425 | 0.4559          | 0.8120   |
| 0.3826        | 20.0  | 1500 | 0.4471          | 0.8170   |
| 0.4327        | 21.0  | 1575 | 0.4442          | 0.8170   |
| 0.3388        | 22.0  | 1650 | 0.4401          | 0.8136   |
| 0.3617        | 23.0  | 1725 | 0.4342          | 0.8186   |
| 0.3721        | 24.0  | 1800 | 0.4302          | 0.8220   |
| 0.3408        | 25.0  | 1875 | 0.4281          | 0.8170   |
| 0.2889        | 26.0  | 1950 | 0.4258          | 0.8203   |
| 0.362         | 27.0  | 2025 | 0.4230          | 0.8220   |
| 0.3321        | 28.0  | 2100 | 0.4203          | 0.8220   |
| 0.3436        | 29.0  | 2175 | 0.4171          | 0.8270   |
| 0.3045        | 30.0  | 2250 | 0.4153          | 0.8286   |
| 0.2874        | 31.0  | 2325 | 0.4166          | 0.8270   |
| 0.2803        | 32.0  | 2400 | 0.4140          | 0.8286   |
| 0.2889        | 33.0  | 2475 | 0.4102          | 0.8303   |
| 0.3058        | 34.0  | 2550 | 0.4088          | 0.8303   |
| 0.2713        | 35.0  | 2625 | 0.4095          | 0.8303   |
| 0.2862        | 36.0  | 2700 | 0.4078          | 0.8303   |
| 0.2924        | 37.0  | 2775 | 0.4065          | 0.8319   |
| 0.3044        | 38.0  | 2850 | 0.4070          | 0.8319   |
| 0.2574        | 39.0  | 2925 | 0.4050          | 0.8319   |
| 0.2876        | 40.0  | 3000 | 0.4054          | 0.8319   |
| 0.3084        | 41.0  | 3075 | 0.4046          | 0.8319   |
| 0.3077        | 42.0  | 3150 | 0.4046          | 0.8319   |
| 0.2848        | 43.0  | 3225 | 0.4035          | 0.8319   |
| 0.2711        | 44.0  | 3300 | 0.4038          | 0.8303   |
| 0.2493        | 45.0  | 3375 | 0.4031          | 0.8319   |
| 0.2526        | 46.0  | 3450 | 0.4030          | 0.8303   |
| 0.2813        | 47.0  | 3525 | 0.4028          | 0.8319   |
| 0.2908        | 48.0  | 3600 | 0.4027          | 0.8303   |
| 0.2758        | 49.0  | 3675 | 0.4027          | 0.8303   |
| 0.3051        | 50.0  | 3750 | 0.4027          | 0.8303   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm