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# smids_5x_beit_base_adamax_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: 1.5977
- Accuracy: 0.8717

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3006        | 1.0   | 375   | 0.3431          | 0.865    |
| 0.2258        | 2.0   | 750   | 0.3974          | 0.8433   |
| 0.1308        | 3.0   | 1125  | 0.4504          | 0.8633   |
| 0.1539        | 4.0   | 1500  | 0.4844          | 0.865    |
| 0.0793        | 5.0   | 1875  | 0.5671          | 0.8683   |
| 0.0565        | 6.0   | 2250  | 0.6745          | 0.865    |
| 0.0735        | 7.0   | 2625  | 0.5104          | 0.8983   |
| 0.0686        | 8.0   | 3000  | 0.7410          | 0.8767   |
| 0.0395        | 9.0   | 3375  | 0.6717          | 0.88     |
| 0.057         | 10.0  | 3750  | 0.5892          | 0.885    |
| 0.0561        | 11.0  | 4125  | 0.7571          | 0.8733   |
| 0.0534        | 12.0  | 4500  | 0.9537          | 0.8583   |
| 0.0346        | 13.0  | 4875  | 0.8297          | 0.85     |
| 0.0136        | 14.0  | 5250  | 0.9195          | 0.8667   |
| 0.0218        | 15.0  | 5625  | 0.9025          | 0.87     |
| 0.0052        | 16.0  | 6000  | 1.0564          | 0.865    |
| 0.0551        | 17.0  | 6375  | 0.9490          | 0.8633   |
| 0.0659        | 18.0  | 6750  | 0.9404          | 0.8717   |
| 0.0263        | 19.0  | 7125  | 0.9533          | 0.875    |
| 0.0009        | 20.0  | 7500  | 0.9914          | 0.87     |
| 0.0008        | 21.0  | 7875  | 1.1404          | 0.855    |
| 0.0028        | 22.0  | 8250  | 1.0363          | 0.8733   |
| 0.0035        | 23.0  | 8625  | 1.0823          | 0.865    |
| 0.0007        | 24.0  | 9000  | 1.0248          | 0.8733   |
| 0.0408        | 25.0  | 9375  | 0.9782          | 0.8683   |
| 0.0262        | 26.0  | 9750  | 1.0392          | 0.8683   |
| 0.0167        | 27.0  | 10125 | 1.0282          | 0.8717   |
| 0.0115        | 28.0  | 10500 | 0.9251          | 0.885    |
| 0.0242        | 29.0  | 10875 | 1.0471          | 0.8767   |
| 0.0002        | 30.0  | 11250 | 1.0729          | 0.8817   |
| 0.0011        | 31.0  | 11625 | 1.1808          | 0.87     |
| 0.0           | 32.0  | 12000 | 1.2243          | 0.8617   |
| 0.0           | 33.0  | 12375 | 1.2017          | 0.8733   |
| 0.0002        | 34.0  | 12750 | 1.2888          | 0.865    |
| 0.0           | 35.0  | 13125 | 1.1910          | 0.8717   |
| 0.0002        | 36.0  | 13500 | 1.3235          | 0.8683   |
| 0.0           | 37.0  | 13875 | 1.2685          | 0.8617   |
| 0.0109        | 38.0  | 14250 | 1.3836          | 0.865    |
| 0.0001        | 39.0  | 14625 | 1.3162          | 0.8733   |
| 0.0002        | 40.0  | 15000 | 1.3327          | 0.86     |
| 0.0           | 41.0  | 15375 | 1.4460          | 0.8567   |
| 0.0           | 42.0  | 15750 | 1.5521          | 0.8583   |
| 0.0           | 43.0  | 16125 | 1.6052          | 0.86     |
| 0.0           | 44.0  | 16500 | 1.5790          | 0.865    |
| 0.0           | 45.0  | 16875 | 1.5481          | 0.8667   |
| 0.0           | 46.0  | 17250 | 1.5829          | 0.8667   |
| 0.0           | 47.0  | 17625 | 1.6010          | 0.8717   |
| 0.0008        | 48.0  | 18000 | 1.5864          | 0.87     |
| 0.0           | 49.0  | 18375 | 1.5922          | 0.8683   |
| 0.0001        | 50.0  | 18750 | 1.5977          | 0.8717   |


### 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