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---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_beit_base_adamax_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7317073170731707
---
<!-- 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. -->
# hushem_1x_beit_base_adamax_001_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: 1.2278
- Accuracy: 0.7317
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4268 | 0.2439 |
| 1.7859 | 2.0 | 12 | 1.3982 | 0.2439 |
| 1.7859 | 3.0 | 18 | 1.3119 | 0.4878 |
| 1.3869 | 4.0 | 24 | 1.2627 | 0.4146 |
| 1.329 | 5.0 | 30 | 1.0564 | 0.5610 |
| 1.329 | 6.0 | 36 | 1.2486 | 0.2927 |
| 1.2971 | 7.0 | 42 | 1.2260 | 0.3415 |
| 1.2971 | 8.0 | 48 | 1.1669 | 0.5122 |
| 1.2043 | 9.0 | 54 | 1.2078 | 0.4390 |
| 1.166 | 10.0 | 60 | 1.1291 | 0.4390 |
| 1.166 | 11.0 | 66 | 1.4793 | 0.2683 |
| 1.2368 | 12.0 | 72 | 1.1712 | 0.4390 |
| 1.2368 | 13.0 | 78 | 1.1600 | 0.4146 |
| 1.0841 | 14.0 | 84 | 1.1286 | 0.4146 |
| 1.1358 | 15.0 | 90 | 1.0309 | 0.4878 |
| 1.1358 | 16.0 | 96 | 1.0536 | 0.3902 |
| 1.0304 | 17.0 | 102 | 0.9535 | 0.4878 |
| 1.0304 | 18.0 | 108 | 1.1738 | 0.3659 |
| 0.9971 | 19.0 | 114 | 0.9220 | 0.5122 |
| 0.9482 | 20.0 | 120 | 1.0234 | 0.6829 |
| 0.9482 | 21.0 | 126 | 1.0465 | 0.5366 |
| 0.9578 | 22.0 | 132 | 1.0713 | 0.5854 |
| 0.9578 | 23.0 | 138 | 1.1190 | 0.5122 |
| 1.0032 | 24.0 | 144 | 1.0303 | 0.6341 |
| 0.9765 | 25.0 | 150 | 0.9143 | 0.6098 |
| 0.9765 | 26.0 | 156 | 0.9675 | 0.6098 |
| 0.8768 | 27.0 | 162 | 0.8561 | 0.6341 |
| 0.8768 | 28.0 | 168 | 1.0406 | 0.4878 |
| 0.813 | 29.0 | 174 | 1.2443 | 0.6098 |
| 0.8566 | 30.0 | 180 | 0.8255 | 0.6341 |
| 0.8566 | 31.0 | 186 | 0.8471 | 0.6829 |
| 0.7675 | 32.0 | 192 | 0.9851 | 0.6829 |
| 0.7675 | 33.0 | 198 | 1.1042 | 0.6829 |
| 0.7167 | 34.0 | 204 | 1.0172 | 0.6829 |
| 0.6799 | 35.0 | 210 | 1.1228 | 0.5366 |
| 0.6799 | 36.0 | 216 | 1.1880 | 0.7317 |
| 0.6558 | 37.0 | 222 | 1.1922 | 0.7317 |
| 0.6558 | 38.0 | 228 | 1.4663 | 0.6585 |
| 0.5997 | 39.0 | 234 | 1.0459 | 0.7317 |
| 0.579 | 40.0 | 240 | 1.1555 | 0.7073 |
| 0.579 | 41.0 | 246 | 1.1889 | 0.7073 |
| 0.5728 | 42.0 | 252 | 1.2278 | 0.7317 |
| 0.5728 | 43.0 | 258 | 1.2278 | 0.7317 |
| 0.5177 | 44.0 | 264 | 1.2278 | 0.7317 |
| 0.5591 | 45.0 | 270 | 1.2278 | 0.7317 |
| 0.5591 | 46.0 | 276 | 1.2278 | 0.7317 |
| 0.5528 | 47.0 | 282 | 1.2278 | 0.7317 |
| 0.5528 | 48.0 | 288 | 1.2278 | 0.7317 |
| 0.575 | 49.0 | 294 | 1.2278 | 0.7317 |
| 0.5528 | 50.0 | 300 | 1.2278 | 0.7317 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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