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# smids_5x_beit_base_adamax_001_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: 0.6098
- Accuracy: 0.8270

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1062        | 1.0   | 375   | 1.0982          | 0.3344   |
| 0.9246        | 2.0   | 750   | 0.8990          | 0.5008   |
| 0.869         | 3.0   | 1125  | 0.8746          | 0.5258   |
| 0.8743        | 4.0   | 1500  | 0.8284          | 0.5674   |
| 0.7741        | 5.0   | 1875  | 0.7937          | 0.5940   |
| 0.7654        | 6.0   | 2250  | 0.8614          | 0.5757   |
| 0.7957        | 7.0   | 2625  | 0.7450          | 0.6273   |
| 0.7361        | 8.0   | 3000  | 0.7601          | 0.6190   |
| 0.7832        | 9.0   | 3375  | 0.7148          | 0.6473   |
| 0.7176        | 10.0  | 3750  | 0.7112          | 0.6539   |
| 0.7021        | 11.0  | 4125  | 0.6760          | 0.6672   |
| 0.7391        | 12.0  | 4500  | 0.6756          | 0.6839   |
| 0.6718        | 13.0  | 4875  | 0.6548          | 0.6955   |
| 0.6958        | 14.0  | 5250  | 0.6500          | 0.7072   |
| 0.5987        | 15.0  | 5625  | 0.6407          | 0.7072   |
| 0.6067        | 16.0  | 6000  | 0.6268          | 0.7221   |
| 0.6185        | 17.0  | 6375  | 0.5757          | 0.7554   |
| 0.5734        | 18.0  | 6750  | 0.5844          | 0.7504   |
| 0.5355        | 19.0  | 7125  | 0.5955          | 0.7354   |
| 0.5827        | 20.0  | 7500  | 0.5704          | 0.7388   |
| 0.5749        | 21.0  | 7875  | 0.5428          | 0.7804   |
| 0.5089        | 22.0  | 8250  | 0.5221          | 0.7887   |
| 0.5094        | 23.0  | 8625  | 0.5782          | 0.7671   |
| 0.5429        | 24.0  | 9000  | 0.5089          | 0.7737   |
| 0.4205        | 25.0  | 9375  | 0.5382          | 0.7687   |
| 0.5532        | 26.0  | 9750  | 0.5416          | 0.7654   |
| 0.5743        | 27.0  | 10125 | 0.5044          | 0.7854   |
| 0.4757        | 28.0  | 10500 | 0.4923          | 0.7837   |
| 0.4893        | 29.0  | 10875 | 0.5093          | 0.7953   |
| 0.4472        | 30.0  | 11250 | 0.4972          | 0.8003   |
| 0.4925        | 31.0  | 11625 | 0.4677          | 0.8003   |
| 0.4327        | 32.0  | 12000 | 0.5055          | 0.8003   |
| 0.3876        | 33.0  | 12375 | 0.5066          | 0.8070   |
| 0.365         | 34.0  | 12750 | 0.5108          | 0.8003   |
| 0.4195        | 35.0  | 13125 | 0.4907          | 0.8136   |
| 0.3672        | 36.0  | 13500 | 0.5164          | 0.8170   |
| 0.3426        | 37.0  | 13875 | 0.5029          | 0.8136   |
| 0.3759        | 38.0  | 14250 | 0.4848          | 0.8220   |
| 0.3673        | 39.0  | 14625 | 0.5029          | 0.7953   |
| 0.3205        | 40.0  | 15000 | 0.4939          | 0.8253   |
| 0.278         | 41.0  | 15375 | 0.4766          | 0.8203   |
| 0.3125        | 42.0  | 15750 | 0.5477          | 0.8170   |
| 0.2792        | 43.0  | 16125 | 0.5391          | 0.8253   |
| 0.2594        | 44.0  | 16500 | 0.5619          | 0.8220   |
| 0.2487        | 45.0  | 16875 | 0.5522          | 0.8153   |
| 0.2997        | 46.0  | 17250 | 0.5706          | 0.8270   |
| 0.2756        | 47.0  | 17625 | 0.5989          | 0.8220   |
| 0.2505        | 48.0  | 18000 | 0.5806          | 0.8303   |
| 0.2151        | 49.0  | 18375 | 0.6077          | 0.8286   |
| 0.1705        | 50.0  | 18750 | 0.6098          | 0.8270   |


### 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