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# smids_5x_beit_base_adamax_001_fold4

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9816
- Accuracy: 0.7917

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8866        | 1.0   | 375   | 0.8458          | 0.5233   |
| 0.8299        | 2.0   | 750   | 0.7752          | 0.57     |
| 0.7493        | 3.0   | 1125  | 0.7658          | 0.615    |
| 0.7146        | 4.0   | 1500  | 0.7493          | 0.6417   |
| 0.7017        | 5.0   | 1875  | 0.7203          | 0.6817   |
| 0.763         | 6.0   | 2250  | 0.6999          | 0.68     |
| 0.6756        | 7.0   | 2625  | 0.6915          | 0.6967   |
| 0.7133        | 8.0   | 3000  | 0.7086          | 0.6683   |
| 0.6524        | 9.0   | 3375  | 0.6683          | 0.6883   |
| 0.7431        | 10.0  | 3750  | 0.6664          | 0.7017   |
| 0.6733        | 11.0  | 4125  | 0.6953          | 0.675    |
| 0.6611        | 12.0  | 4500  | 0.6840          | 0.6933   |
| 0.6809        | 13.0  | 4875  | 0.6889          | 0.6717   |
| 0.6678        | 14.0  | 5250  | 0.6820          | 0.6917   |
| 0.6888        | 15.0  | 5625  | 0.6381          | 0.7083   |
| 0.6174        | 16.0  | 6000  | 0.6105          | 0.7417   |
| 0.6389        | 17.0  | 6375  | 0.6744          | 0.7033   |
| 0.6613        | 18.0  | 6750  | 0.6040          | 0.7417   |
| 0.6398        | 19.0  | 7125  | 0.6119          | 0.7317   |
| 0.6011        | 20.0  | 7500  | 0.5771          | 0.7517   |
| 0.5356        | 21.0  | 7875  | 0.6886          | 0.7033   |
| 0.5588        | 22.0  | 8250  | 0.6145          | 0.7433   |
| 0.5597        | 23.0  | 8625  | 0.6084          | 0.7383   |
| 0.5734        | 24.0  | 9000  | 0.5790          | 0.7633   |
| 0.5451        | 25.0  | 9375  | 0.5688          | 0.7567   |
| 0.5084        | 26.0  | 9750  | 0.5594          | 0.765    |
| 0.4925        | 27.0  | 10125 | 0.6035          | 0.7633   |
| 0.4449        | 28.0  | 10500 | 0.5736          | 0.7717   |
| 0.4935        | 29.0  | 10875 | 0.5611          | 0.76     |
| 0.518         | 30.0  | 11250 | 0.6001          | 0.75     |
| 0.5374        | 31.0  | 11625 | 0.5726          | 0.7867   |
| 0.4582        | 32.0  | 12000 | 0.5878          | 0.7717   |
| 0.4449        | 33.0  | 12375 | 0.6022          | 0.77     |
| 0.4991        | 34.0  | 12750 | 0.5950          | 0.7833   |
| 0.3652        | 35.0  | 13125 | 0.5638          | 0.8033   |
| 0.4263        | 36.0  | 13500 | 0.5959          | 0.7883   |
| 0.4604        | 37.0  | 13875 | 0.6072          | 0.8      |
| 0.4152        | 38.0  | 14250 | 0.6172          | 0.8033   |
| 0.3735        | 39.0  | 14625 | 0.6726          | 0.79     |
| 0.3648        | 40.0  | 15000 | 0.6751          | 0.7933   |
| 0.3489        | 41.0  | 15375 | 0.6954          | 0.7833   |
| 0.235         | 42.0  | 15750 | 0.7474          | 0.7767   |
| 0.2834        | 43.0  | 16125 | 0.7611          | 0.7933   |
| 0.2126        | 44.0  | 16500 | 0.7672          | 0.7917   |
| 0.2122        | 45.0  | 16875 | 0.8481          | 0.7683   |
| 0.1955        | 46.0  | 17250 | 0.8595          | 0.795    |
| 0.1764        | 47.0  | 17625 | 0.8929          | 0.7917   |
| 0.2086        | 48.0  | 18000 | 0.9496          | 0.79     |
| 0.175         | 49.0  | 18375 | 0.9722          | 0.7917   |
| 0.152         | 50.0  | 18750 | 0.9816          | 0.7917   |


### 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