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# smids_5x_deit_base_adamax_001_fold1

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: 0.7497
- Accuracy: 0.9132

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.329         | 1.0   | 376   | 0.4277          | 0.8464   |
| 0.2087        | 2.0   | 752   | 0.3407          | 0.8698   |
| 0.2485        | 3.0   | 1128  | 0.3788          | 0.8598   |
| 0.178         | 4.0   | 1504  | 0.4197          | 0.8531   |
| 0.1258        | 5.0   | 1880  | 0.4173          | 0.8648   |
| 0.1206        | 6.0   | 2256  | 0.3586          | 0.8848   |
| 0.1282        | 7.0   | 2632  | 0.3517          | 0.8865   |
| 0.0583        | 8.0   | 3008  | 0.5359          | 0.8765   |
| 0.0747        | 9.0   | 3384  | 0.5100          | 0.8731   |
| 0.0435        | 10.0  | 3760  | 0.5516          | 0.8781   |
| 0.06          | 11.0  | 4136  | 0.3933          | 0.8998   |
| 0.0257        | 12.0  | 4512  | 0.5267          | 0.8848   |
| 0.0686        | 13.0  | 4888  | 0.4896          | 0.9065   |
| 0.016         | 14.0  | 5264  | 0.5666          | 0.8881   |
| 0.011         | 15.0  | 5640  | 0.5612          | 0.8965   |
| 0.0019        | 16.0  | 6016  | 0.6453          | 0.8848   |
| 0.0015        | 17.0  | 6392  | 0.5726          | 0.8948   |
| 0.0354        | 18.0  | 6768  | 0.5332          | 0.9048   |
| 0.0037        | 19.0  | 7144  | 0.5726          | 0.8965   |
| 0.0094        | 20.0  | 7520  | 0.5926          | 0.9032   |
| 0.0008        | 21.0  | 7896  | 0.5520          | 0.8998   |
| 0.0004        | 22.0  | 8272  | 0.4436          | 0.9165   |
| 0.0006        | 23.0  | 8648  | 0.6077          | 0.8965   |
| 0.001         | 24.0  | 9024  | 0.6248          | 0.9132   |
| 0.0003        | 25.0  | 9400  | 0.6715          | 0.8982   |
| 0.0035        | 26.0  | 9776  | 0.6641          | 0.9082   |
| 0.0           | 27.0  | 10152 | 0.6982          | 0.9048   |
| 0.0           | 28.0  | 10528 | 0.7269          | 0.8982   |
| 0.0054        | 29.0  | 10904 | 0.6756          | 0.9098   |
| 0.0034        | 30.0  | 11280 | 0.6451          | 0.9065   |
| 0.0           | 31.0  | 11656 | 0.6535          | 0.9098   |
| 0.0           | 32.0  | 12032 | 0.6650          | 0.9065   |
| 0.0           | 33.0  | 12408 | 0.6759          | 0.9082   |
| 0.0           | 34.0  | 12784 | 0.6731          | 0.9048   |
| 0.0           | 35.0  | 13160 | 0.6782          | 0.9082   |
| 0.0001        | 36.0  | 13536 | 0.6755          | 0.9032   |
| 0.0           | 37.0  | 13912 | 0.7594          | 0.9098   |
| 0.0           | 38.0  | 14288 | 0.7065          | 0.9115   |
| 0.0           | 39.0  | 14664 | 0.7005          | 0.9082   |
| 0.0           | 40.0  | 15040 | 0.7058          | 0.9149   |
| 0.0           | 41.0  | 15416 | 0.6924          | 0.9115   |
| 0.0           | 42.0  | 15792 | 0.7078          | 0.9149   |
| 0.0           | 43.0  | 16168 | 0.7156          | 0.9149   |
| 0.0           | 44.0  | 16544 | 0.7204          | 0.9165   |
| 0.0           | 45.0  | 16920 | 0.7358          | 0.9149   |
| 0.003         | 46.0  | 17296 | 0.7278          | 0.9165   |
| 0.0           | 47.0  | 17672 | 0.7349          | 0.9149   |
| 0.0           | 48.0  | 18048 | 0.7414          | 0.9149   |
| 0.0           | 49.0  | 18424 | 0.7464          | 0.9149   |
| 0.0023        | 50.0  | 18800 | 0.7497          | 0.9132   |


### 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