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# smids_5x_deit_base_rms_001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7864
- Accuracy: 0.775

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8829        | 1.0   | 375   | 0.8947          | 0.4967   |
| 0.7778        | 2.0   | 750   | 0.8858          | 0.5283   |
| 0.8311        | 3.0   | 1125  | 0.8355          | 0.55     |
| 0.8294        | 4.0   | 1500  | 0.7887          | 0.6067   |
| 0.8799        | 5.0   | 1875  | 0.8054          | 0.6217   |
| 0.7828        | 6.0   | 2250  | 0.8032          | 0.5883   |
| 0.7591        | 7.0   | 2625  | 0.7342          | 0.6533   |
| 0.7195        | 8.0   | 3000  | 0.7320          | 0.6317   |
| 0.6862        | 9.0   | 3375  | 0.8798          | 0.5583   |
| 0.6609        | 10.0  | 3750  | 0.6983          | 0.6717   |
| 0.6813        | 11.0  | 4125  | 0.7308          | 0.67     |
| 0.7021        | 12.0  | 4500  | 0.7207          | 0.63     |
| 0.6223        | 13.0  | 4875  | 0.6947          | 0.685    |
| 0.5883        | 14.0  | 5250  | 0.6340          | 0.7217   |
| 0.6307        | 15.0  | 5625  | 0.6616          | 0.7033   |
| 0.6001        | 16.0  | 6000  | 0.6868          | 0.6983   |
| 0.6448        | 17.0  | 6375  | 0.6323          | 0.7233   |
| 0.6618        | 18.0  | 6750  | 0.6385          | 0.7233   |
| 0.5927        | 19.0  | 7125  | 0.6305          | 0.71     |
| 0.5637        | 20.0  | 7500  | 0.6279          | 0.7033   |
| 0.5052        | 21.0  | 7875  | 0.6248          | 0.7217   |
| 0.5232        | 22.0  | 8250  | 0.6213          | 0.7133   |
| 0.586         | 23.0  | 8625  | 0.6387          | 0.7217   |
| 0.6497        | 24.0  | 9000  | 0.5879          | 0.7367   |
| 0.5103        | 25.0  | 9375  | 0.6071          | 0.7317   |
| 0.5081        | 26.0  | 9750  | 0.6415          | 0.725    |
| 0.5448        | 27.0  | 10125 | 0.5744          | 0.725    |
| 0.5273        | 28.0  | 10500 | 0.6005          | 0.7333   |
| 0.5173        | 29.0  | 10875 | 0.6315          | 0.725    |
| 0.4436        | 30.0  | 11250 | 0.5604          | 0.7617   |
| 0.5135        | 31.0  | 11625 | 0.5861          | 0.7633   |
| 0.5481        | 32.0  | 12000 | 0.5892          | 0.7467   |
| 0.5389        | 33.0  | 12375 | 0.5940          | 0.7533   |
| 0.5388        | 34.0  | 12750 | 0.5721          | 0.74     |
| 0.4177        | 35.0  | 13125 | 0.6397          | 0.7317   |
| 0.4044        | 36.0  | 13500 | 0.6157          | 0.74     |
| 0.4195        | 37.0  | 13875 | 0.6245          | 0.75     |
| 0.4242        | 38.0  | 14250 | 0.5771          | 0.7567   |
| 0.4027        | 39.0  | 14625 | 0.5507          | 0.7533   |
| 0.3811        | 40.0  | 15000 | 0.6123          | 0.7483   |
| 0.3735        | 41.0  | 15375 | 0.6056          | 0.7583   |
| 0.3892        | 42.0  | 15750 | 0.6319          | 0.7633   |
| 0.3928        | 43.0  | 16125 | 0.6475          | 0.7683   |
| 0.3524        | 44.0  | 16500 | 0.6335          | 0.7783   |
| 0.2896        | 45.0  | 16875 | 0.6766          | 0.7683   |
| 0.3014        | 46.0  | 17250 | 0.7029          | 0.775    |
| 0.2555        | 47.0  | 17625 | 0.6873          | 0.7967   |
| 0.2601        | 48.0  | 18000 | 0.7571          | 0.78     |
| 0.2142        | 49.0  | 18375 | 0.7689          | 0.7833   |
| 0.2176        | 50.0  | 18750 | 0.7864          | 0.775    |


### 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