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# smids_5x_beit_base_adamax_001_fold5

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: 0.6041
- Accuracy: 0.7617

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8603        | 1.0   | 375   | 0.8648          | 0.5183   |
| 0.8445        | 2.0   | 750   | 0.8098          | 0.5417   |
| 0.7944        | 3.0   | 1125  | 0.7826          | 0.5917   |
| 0.7602        | 4.0   | 1500  | 0.8095          | 0.6133   |
| 0.7358        | 5.0   | 1875  | 0.7702          | 0.62     |
| 0.7338        | 6.0   | 2250  | 0.7325          | 0.6383   |
| 0.7068        | 7.0   | 2625  | 0.7570          | 0.6267   |
| 0.7788        | 8.0   | 3000  | 0.7318          | 0.6183   |
| 0.7701        | 9.0   | 3375  | 0.7391          | 0.65     |
| 0.7025        | 10.0  | 3750  | 0.7251          | 0.6617   |
| 0.7076        | 11.0  | 4125  | 0.7171          | 0.6433   |
| 0.6226        | 12.0  | 4500  | 0.7139          | 0.6333   |
| 0.6825        | 13.0  | 4875  | 0.7299          | 0.63     |
| 0.6882        | 14.0  | 5250  | 0.7324          | 0.6517   |
| 0.7468        | 15.0  | 5625  | 0.6842          | 0.7      |
| 0.6568        | 16.0  | 6000  | 0.7213          | 0.6533   |
| 0.6593        | 17.0  | 6375  | 0.6880          | 0.6583   |
| 0.68          | 18.0  | 6750  | 0.6884          | 0.6733   |
| 0.6767        | 19.0  | 7125  | 0.7231          | 0.665    |
| 0.6609        | 20.0  | 7500  | 0.6577          | 0.6983   |
| 0.6233        | 21.0  | 7875  | 0.7352          | 0.6417   |
| 0.6128        | 22.0  | 8250  | 0.6662          | 0.695    |
| 0.6939        | 23.0  | 8625  | 0.7254          | 0.71     |
| 0.6892        | 24.0  | 9000  | 0.7067          | 0.695    |
| 0.5723        | 25.0  | 9375  | 0.6348          | 0.72     |
| 0.6474        | 26.0  | 9750  | 0.6506          | 0.7083   |
| 0.6695        | 27.0  | 10125 | 0.6672          | 0.6883   |
| 0.7033        | 28.0  | 10500 | 0.6914          | 0.6833   |
| 0.6792        | 29.0  | 10875 | 0.6764          | 0.685    |
| 0.5904        | 30.0  | 11250 | 0.6857          | 0.6883   |
| 0.5913        | 31.0  | 11625 | 0.6709          | 0.6933   |
| 0.5784        | 32.0  | 12000 | 0.7184          | 0.69     |
| 0.6212        | 33.0  | 12375 | 0.6393          | 0.7233   |
| 0.6674        | 34.0  | 12750 | 0.6697          | 0.71     |
| 0.5844        | 35.0  | 13125 | 0.6220          | 0.7283   |
| 0.5892        | 36.0  | 13500 | 0.6265          | 0.7217   |
| 0.572         | 37.0  | 13875 | 0.6315          | 0.7117   |
| 0.5345        | 38.0  | 14250 | 0.6267          | 0.7417   |
| 0.5582        | 39.0  | 14625 | 0.5952          | 0.7433   |
| 0.5947        | 40.0  | 15000 | 0.6182          | 0.715    |
| 0.5681        | 41.0  | 15375 | 0.6009          | 0.7533   |
| 0.5885        | 42.0  | 15750 | 0.6107          | 0.7367   |
| 0.5772        | 43.0  | 16125 | 0.5746          | 0.75     |
| 0.4378        | 44.0  | 16500 | 0.5833          | 0.755    |
| 0.5286        | 45.0  | 16875 | 0.6256          | 0.7417   |
| 0.538         | 46.0  | 17250 | 0.6036          | 0.7483   |
| 0.5732        | 47.0  | 17625 | 0.6044          | 0.76     |
| 0.4485        | 48.0  | 18000 | 0.5966          | 0.7533   |
| 0.4959        | 49.0  | 18375 | 0.6043          | 0.7583   |
| 0.4683        | 50.0  | 18750 | 0.6041          | 0.7617   |


### 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