
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_5x_beit_base_adamax_00001_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: 0.7406
- Accuracy: 0.9083

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2292        | 1.0   | 375   | 0.3140          | 0.8683   |
| 0.2092        | 2.0   | 750   | 0.2336          | 0.9067   |
| 0.1226        | 3.0   | 1125  | 0.2477          | 0.9117   |
| 0.0876        | 4.0   | 1500  | 0.2419          | 0.92     |
| 0.0679        | 5.0   | 1875  | 0.3315          | 0.8983   |
| 0.0323        | 6.0   | 2250  | 0.3576          | 0.905    |
| 0.0879        | 7.0   | 2625  | 0.4064          | 0.91     |
| 0.0394        | 8.0   | 3000  | 0.4497          | 0.91     |
| 0.0359        | 9.0   | 3375  | 0.4692          | 0.915    |
| 0.0158        | 10.0  | 3750  | 0.5082          | 0.9117   |
| 0.0543        | 11.0  | 4125  | 0.4628          | 0.92     |
| 0.0223        | 12.0  | 4500  | 0.5427          | 0.915    |
| 0.0015        | 13.0  | 4875  | 0.6163          | 0.905    |
| 0.0283        | 14.0  | 5250  | 0.6260          | 0.91     |
| 0.0146        | 15.0  | 5625  | 0.5842          | 0.9083   |
| 0.009         | 16.0  | 6000  | 0.6137          | 0.91     |
| 0.0335        | 17.0  | 6375  | 0.6438          | 0.905    |
| 0.0046        | 18.0  | 6750  | 0.6182          | 0.91     |
| 0.0123        | 19.0  | 7125  | 0.7222          | 0.8983   |
| 0.0002        | 20.0  | 7500  | 0.6977          | 0.9067   |
| 0.0066        | 21.0  | 7875  | 0.7063          | 0.915    |
| 0.0363        | 22.0  | 8250  | 0.6479          | 0.9083   |
| 0.0494        | 23.0  | 8625  | 0.6537          | 0.905    |
| 0.0006        | 24.0  | 9000  | 0.7370          | 0.9067   |
| 0.0292        | 25.0  | 9375  | 0.6958          | 0.9      |
| 0.0254        | 26.0  | 9750  | 0.7175          | 0.8983   |
| 0.0001        | 27.0  | 10125 | 0.7650          | 0.9017   |
| 0.0095        | 28.0  | 10500 | 0.7329          | 0.9067   |
| 0.0232        | 29.0  | 10875 | 0.6879          | 0.9033   |
| 0.0172        | 30.0  | 11250 | 0.7353          | 0.905    |
| 0.0378        | 31.0  | 11625 | 0.7667          | 0.9117   |
| 0.0068        | 32.0  | 12000 | 0.7535          | 0.9083   |
| 0.0003        | 33.0  | 12375 | 0.7493          | 0.9133   |
| 0.0005        | 34.0  | 12750 | 0.7396          | 0.9067   |
| 0.013         | 35.0  | 13125 | 0.7540          | 0.9083   |
| 0.0005        | 36.0  | 13500 | 0.7288          | 0.9083   |
| 0.0073        | 37.0  | 13875 | 0.7577          | 0.9067   |
| 0.0001        | 38.0  | 14250 | 0.7421          | 0.9033   |
| 0.0156        | 39.0  | 14625 | 0.7370          | 0.9067   |
| 0.0137        | 40.0  | 15000 | 0.7555          | 0.905    |
| 0.0           | 41.0  | 15375 | 0.7434          | 0.905    |
| 0.0           | 42.0  | 15750 | 0.7571          | 0.9017   |
| 0.0002        | 43.0  | 16125 | 0.7615          | 0.9033   |
| 0.0008        | 44.0  | 16500 | 0.7655          | 0.9033   |
| 0.0           | 45.0  | 16875 | 0.7629          | 0.905    |
| 0.0005        | 46.0  | 17250 | 0.7484          | 0.9117   |
| 0.0132        | 47.0  | 17625 | 0.7394          | 0.9083   |
| 0.0           | 48.0  | 18000 | 0.7491          | 0.91     |
| 0.0001        | 49.0  | 18375 | 0.7435          | 0.91     |
| 0.017         | 50.0  | 18750 | 0.7406          | 0.9083   |


### 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