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# hushem_40x_beit_large_adamax_001_fold5

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1906
- Accuracy: 0.8049

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3728        | 1.0   | 220   | 0.2484          | 0.9024   |
| 0.2424        | 2.0   | 440   | 1.0593          | 0.7805   |
| 0.1221        | 3.0   | 660   | 0.9944          | 0.7317   |
| 0.0746        | 4.0   | 880   | 1.4179          | 0.7073   |
| 0.0501        | 5.0   | 1100  | 0.6557          | 0.8049   |
| 0.0914        | 6.0   | 1320  | 1.5051          | 0.7073   |
| 0.0408        | 7.0   | 1540  | 0.1238          | 0.9512   |
| 0.0281        | 8.0   | 1760  | 0.6572          | 0.8537   |
| 0.0024        | 9.0   | 1980  | 0.9478          | 0.8049   |
| 0.0097        | 10.0  | 2200  | 0.6899          | 0.8537   |
| 0.0507        | 11.0  | 2420  | 1.0591          | 0.8049   |
| 0.0001        | 12.0  | 2640  | 0.9070          | 0.8780   |
| 0.0056        | 13.0  | 2860  | 1.1233          | 0.7805   |
| 0.0168        | 14.0  | 3080  | 1.3279          | 0.8049   |
| 0.0205        | 15.0  | 3300  | 1.4696          | 0.8049   |
| 0.0004        | 16.0  | 3520  | 1.8691          | 0.7561   |
| 0.0001        | 17.0  | 3740  | 1.4193          | 0.8293   |
| 0.0029        | 18.0  | 3960  | 1.9471          | 0.8049   |
| 0.0           | 19.0  | 4180  | 1.9190          | 0.7317   |
| 0.0           | 20.0  | 4400  | 2.0689          | 0.7317   |
| 0.0021        | 21.0  | 4620  | 0.3369          | 0.9024   |
| 0.0001        | 22.0  | 4840  | 0.9862          | 0.8537   |
| 0.0001        | 23.0  | 5060  | 0.9863          | 0.8780   |
| 0.0118        | 24.0  | 5280  | 1.0405          | 0.8049   |
| 0.0016        | 25.0  | 5500  | 1.4400          | 0.7805   |
| 0.0379        | 26.0  | 5720  | 1.0773          | 0.8537   |
| 0.0           | 27.0  | 5940  | 0.9902          | 0.8537   |
| 0.0           | 28.0  | 6160  | 0.9125          | 0.8293   |
| 0.0           | 29.0  | 6380  | 0.8492          | 0.8293   |
| 0.0           | 30.0  | 6600  | 1.3170          | 0.8293   |
| 0.0           | 31.0  | 6820  | 1.3145          | 0.7805   |
| 0.0           | 32.0  | 7040  | 0.7274          | 0.8780   |
| 0.0           | 33.0  | 7260  | 0.7992          | 0.8780   |
| 0.0           | 34.0  | 7480  | 0.7001          | 0.9024   |
| 0.0           | 35.0  | 7700  | 0.7059          | 0.9024   |
| 0.0           | 36.0  | 7920  | 0.7509          | 0.9024   |
| 0.0           | 37.0  | 8140  | 0.7646          | 0.9024   |
| 0.0           | 38.0  | 8360  | 1.2149          | 0.8293   |
| 0.0           | 39.0  | 8580  | 1.2146          | 0.8293   |
| 0.0           | 40.0  | 8800  | 1.2180          | 0.8293   |
| 0.0           | 41.0  | 9020  | 1.1864          | 0.8049   |
| 0.0           | 42.0  | 9240  | 1.1736          | 0.8049   |
| 0.0           | 43.0  | 9460  | 1.1601          | 0.8049   |
| 0.0           | 44.0  | 9680  | 1.1683          | 0.8049   |
| 0.0           | 45.0  | 9900  | 1.1682          | 0.8049   |
| 0.0           | 46.0  | 10120 | 1.1690          | 0.8049   |
| 0.0           | 47.0  | 10340 | 1.1691          | 0.8049   |
| 0.0           | 48.0  | 10560 | 1.1738          | 0.8049   |
| 0.0           | 49.0  | 10780 | 1.1753          | 0.8049   |
| 0.0           | 50.0  | 11000 | 1.1906          | 0.8049   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

01_normal 02_tapered 03_pyriform 04_amorphous