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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_5x_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: 0.7804878048780488
---
<!-- 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_5x_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: 2.4090
- Accuracy: 0.7805
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4173 | 1.0 | 28 | 1.3891 | 0.2683 |
| 1.3694 | 2.0 | 56 | 1.3106 | 0.2927 |
| 1.1585 | 3.0 | 84 | 1.3727 | 0.5610 |
| 1.1633 | 4.0 | 112 | 0.9530 | 0.6341 |
| 1.0808 | 5.0 | 140 | 0.8248 | 0.7073 |
| 1.0058 | 6.0 | 168 | 0.6596 | 0.7073 |
| 0.8888 | 7.0 | 196 | 0.8270 | 0.6829 |
| 0.8643 | 8.0 | 224 | 1.1710 | 0.5122 |
| 0.8719 | 9.0 | 252 | 0.8636 | 0.6098 |
| 0.9495 | 10.0 | 280 | 0.6951 | 0.7073 |
| 0.7539 | 11.0 | 308 | 0.7129 | 0.8049 |
| 0.7103 | 12.0 | 336 | 1.1463 | 0.5366 |
| 0.8944 | 13.0 | 364 | 0.9066 | 0.6829 |
| 0.8497 | 14.0 | 392 | 0.8746 | 0.7073 |
| 0.79 | 15.0 | 420 | 1.0867 | 0.6341 |
| 0.7113 | 16.0 | 448 | 0.8154 | 0.7073 |
| 0.7564 | 17.0 | 476 | 0.7453 | 0.7561 |
| 0.6147 | 18.0 | 504 | 1.0583 | 0.6098 |
| 0.7024 | 19.0 | 532 | 0.9615 | 0.6829 |
| 0.7327 | 20.0 | 560 | 1.0915 | 0.6098 |
| 0.5576 | 21.0 | 588 | 0.9041 | 0.7561 |
| 0.4937 | 22.0 | 616 | 1.0076 | 0.8049 |
| 0.5781 | 23.0 | 644 | 1.0524 | 0.6829 |
| 0.478 | 24.0 | 672 | 1.0298 | 0.7561 |
| 0.5392 | 25.0 | 700 | 1.0140 | 0.6585 |
| 0.3827 | 26.0 | 728 | 1.6432 | 0.7317 |
| 0.3978 | 27.0 | 756 | 1.4850 | 0.7561 |
| 0.3605 | 28.0 | 784 | 1.3340 | 0.7805 |
| 0.2382 | 29.0 | 812 | 1.4757 | 0.7805 |
| 0.2077 | 30.0 | 840 | 2.1685 | 0.7317 |
| 0.2429 | 31.0 | 868 | 1.3423 | 0.7805 |
| 0.2302 | 32.0 | 896 | 1.8898 | 0.7561 |
| 0.1961 | 33.0 | 924 | 1.4382 | 0.7805 |
| 0.1775 | 34.0 | 952 | 1.8008 | 0.7561 |
| 0.1314 | 35.0 | 980 | 1.9048 | 0.7317 |
| 0.0435 | 36.0 | 1008 | 2.0856 | 0.7317 |
| 0.1658 | 37.0 | 1036 | 2.4005 | 0.7561 |
| 0.0258 | 38.0 | 1064 | 2.3634 | 0.7805 |
| 0.0985 | 39.0 | 1092 | 2.3142 | 0.7561 |
| 0.0844 | 40.0 | 1120 | 2.5789 | 0.7073 |
| 0.0832 | 41.0 | 1148 | 2.3270 | 0.7805 |
| 0.0163 | 42.0 | 1176 | 2.1273 | 0.8293 |
| 0.0187 | 43.0 | 1204 | 2.3057 | 0.7805 |
| 0.0207 | 44.0 | 1232 | 2.3431 | 0.7561 |
| 0.0233 | 45.0 | 1260 | 2.3612 | 0.7317 |
| 0.0252 | 46.0 | 1288 | 2.4095 | 0.7317 |
| 0.0208 | 47.0 | 1316 | 2.3721 | 0.7805 |
| 0.0009 | 48.0 | 1344 | 2.4085 | 0.7805 |
| 0.0012 | 49.0 | 1372 | 2.4090 | 0.7805 |
| 0.0004 | 50.0 | 1400 | 2.4090 | 0.7805 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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