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# smids_5x_beit_base_adamax_0001_fold2

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: 1.0996
- Accuracy: 0.9068

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3388        | 1.0   | 375   | 0.2592          | 0.8918   |
| 0.2336        | 2.0   | 750   | 0.6135          | 0.8403   |
| 0.171         | 3.0   | 1125  | 0.4021          | 0.8636   |
| 0.1223        | 4.0   | 1500  | 0.5420          | 0.8769   |
| 0.1021        | 5.0   | 1875  | 0.4050          | 0.9018   |
| 0.1121        | 6.0   | 2250  | 0.4217          | 0.8952   |
| 0.1055        | 7.0   | 2625  | 0.5917          | 0.8785   |
| 0.0936        | 8.0   | 3000  | 0.5515          | 0.8902   |
| 0.0292        | 9.0   | 3375  | 0.5916          | 0.8952   |
| 0.0288        | 10.0  | 3750  | 0.4755          | 0.9002   |
| 0.0249        | 11.0  | 4125  | 0.6337          | 0.8702   |
| 0.0449        | 12.0  | 4500  | 0.5405          | 0.8918   |
| 0.0538        | 13.0  | 4875  | 0.7447          | 0.8769   |
| 0.0351        | 14.0  | 5250  | 0.7235          | 0.8852   |
| 0.0643        | 15.0  | 5625  | 0.8669          | 0.8686   |
| 0.0207        | 16.0  | 6000  | 0.8090          | 0.8752   |
| 0.0143        | 17.0  | 6375  | 0.7492          | 0.8802   |
| 0.0274        | 18.0  | 6750  | 0.6970          | 0.8852   |
| 0.033         | 19.0  | 7125  | 0.6368          | 0.8819   |
| 0.0159        | 20.0  | 7500  | 0.6552          | 0.8735   |
| 0.0363        | 21.0  | 7875  | 0.6430          | 0.8819   |
| 0.0001        | 22.0  | 8250  | 0.7685          | 0.8885   |
| 0.0004        | 23.0  | 8625  | 0.8828          | 0.8735   |
| 0.0808        | 24.0  | 9000  | 0.5316          | 0.8918   |
| 0.0051        | 25.0  | 9375  | 0.9086          | 0.8769   |
| 0.0009        | 26.0  | 9750  | 0.7387          | 0.8835   |
| 0.014         | 27.0  | 10125 | 0.7630          | 0.9035   |
| 0.0182        | 28.0  | 10500 | 0.6195          | 0.9035   |
| 0.0001        | 29.0  | 10875 | 0.8510          | 0.8952   |
| 0.0216        | 30.0  | 11250 | 0.8683          | 0.8785   |
| 0.0043        | 31.0  | 11625 | 0.7909          | 0.8985   |
| 0.0064        | 32.0  | 12000 | 0.9052          | 0.8902   |
| 0.0153        | 33.0  | 12375 | 0.8789          | 0.8968   |
| 0.0001        | 34.0  | 12750 | 0.8661          | 0.8935   |
| 0.0           | 35.0  | 13125 | 0.8999          | 0.8885   |
| 0.0           | 36.0  | 13500 | 1.0043          | 0.8918   |
| 0.0001        | 37.0  | 13875 | 0.9274          | 0.8968   |
| 0.0113        | 38.0  | 14250 | 0.8785          | 0.9052   |
| 0.0           | 39.0  | 14625 | 0.9514          | 0.9018   |
| 0.0025        | 40.0  | 15000 | 1.0212          | 0.9052   |
| 0.0           | 41.0  | 15375 | 0.9947          | 0.8952   |
| 0.0027        | 42.0  | 15750 | 0.9897          | 0.9018   |
| 0.0024        | 43.0  | 16125 | 0.9906          | 0.9085   |
| 0.0024        | 44.0  | 16500 | 1.0802          | 0.8952   |
| 0.0028        | 45.0  | 16875 | 1.0813          | 0.8985   |
| 0.0           | 46.0  | 17250 | 1.0748          | 0.8985   |
| 0.0059        | 47.0  | 17625 | 1.0853          | 0.9052   |
| 0.0           | 48.0  | 18000 | 1.1050          | 0.9068   |
| 0.0036        | 49.0  | 18375 | 1.1002          | 0.9052   |
| 0.0022        | 50.0  | 18750 | 1.0996          | 0.9068   |


### 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