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# smids_5x_beit_base_rms_0001_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: 1.5932
- Accuracy: 0.87

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3203        | 1.0   | 375   | 0.4417          | 0.8283   |
| 0.2426        | 2.0   | 750   | 0.3953          | 0.8467   |
| 0.1231        | 3.0   | 1125  | 0.5794          | 0.86     |
| 0.1615        | 4.0   | 1500  | 0.5639          | 0.8417   |
| 0.0891        | 5.0   | 1875  | 0.5564          | 0.8583   |
| 0.0575        | 6.0   | 2250  | 0.6614          | 0.8617   |
| 0.0358        | 7.0   | 2625  | 0.6797          | 0.8667   |
| 0.0806        | 8.0   | 3000  | 0.6344          | 0.865    |
| 0.0789        | 9.0   | 3375  | 0.6957          | 0.8717   |
| 0.0241        | 10.0  | 3750  | 0.9064          | 0.8567   |
| 0.0385        | 11.0  | 4125  | 0.7288          | 0.865    |
| 0.0734        | 12.0  | 4500  | 0.9419          | 0.8617   |
| 0.0068        | 13.0  | 4875  | 1.1180          | 0.8417   |
| 0.0709        | 14.0  | 5250  | 0.8470          | 0.8517   |
| 0.0043        | 15.0  | 5625  | 0.9006          | 0.8733   |
| 0.0276        | 16.0  | 6000  | 0.9685          | 0.8617   |
| 0.0864        | 17.0  | 6375  | 0.9433          | 0.865    |
| 0.0263        | 18.0  | 6750  | 1.0594          | 0.8667   |
| 0.0288        | 19.0  | 7125  | 0.9857          | 0.8617   |
| 0.0437        | 20.0  | 7500  | 0.9535          | 0.8617   |
| 0.0182        | 21.0  | 7875  | 1.0851          | 0.8633   |
| 0.0247        | 22.0  | 8250  | 0.9263          | 0.8683   |
| 0.0006        | 23.0  | 8625  | 0.9868          | 0.8717   |
| 0.0399        | 24.0  | 9000  | 1.0128          | 0.86     |
| 0.0299        | 25.0  | 9375  | 0.9782          | 0.8867   |
| 0.0002        | 26.0  | 9750  | 1.2403          | 0.8567   |
| 0.0051        | 27.0  | 10125 | 1.1638          | 0.8567   |
| 0.0126        | 28.0  | 10500 | 1.0178          | 0.87     |
| 0.0001        | 29.0  | 10875 | 1.0674          | 0.8717   |
| 0.008         | 30.0  | 11250 | 1.0641          | 0.8633   |
| 0.0072        | 31.0  | 11625 | 1.1590          | 0.8667   |
| 0.0003        | 32.0  | 12000 | 1.3669          | 0.8567   |
| 0.0001        | 33.0  | 12375 | 1.2305          | 0.8733   |
| 0.0072        | 34.0  | 12750 | 1.1660          | 0.865    |
| 0.0           | 35.0  | 13125 | 1.3099          | 0.8683   |
| 0.001         | 36.0  | 13500 | 1.3895          | 0.8617   |
| 0.0003        | 37.0  | 13875 | 1.2632          | 0.8633   |
| 0.0099        | 38.0  | 14250 | 1.2864          | 0.8583   |
| 0.0           | 39.0  | 14625 | 1.2372          | 0.87     |
| 0.0           | 40.0  | 15000 | 1.3431          | 0.85     |
| 0.0           | 41.0  | 15375 | 1.3348          | 0.86     |
| 0.0           | 42.0  | 15750 | 1.4149          | 0.8633   |
| 0.0           | 43.0  | 16125 | 1.5031          | 0.86     |
| 0.0           | 44.0  | 16500 | 1.5165          | 0.8667   |
| 0.0           | 45.0  | 16875 | 1.5362          | 0.8633   |
| 0.0           | 46.0  | 17250 | 1.5276          | 0.865    |
| 0.0           | 47.0  | 17625 | 1.5857          | 0.8667   |
| 0.0           | 48.0  | 18000 | 1.5842          | 0.8667   |
| 0.0           | 49.0  | 18375 | 1.5927          | 0.8683   |
| 0.0           | 50.0  | 18750 | 1.5932          | 0.87     |


### 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