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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_00001_fold3
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: 0.9069767441860465
---
<!-- 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_00001_fold3
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: 0.4974
- Accuracy: 0.9070
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2118 | 1.0 | 28 | 1.1325 | 0.6047 |
| 0.7198 | 2.0 | 56 | 0.8388 | 0.6744 |
| 0.3881 | 3.0 | 84 | 0.6224 | 0.7674 |
| 0.2597 | 4.0 | 112 | 0.4679 | 0.8140 |
| 0.1551 | 5.0 | 140 | 0.4682 | 0.8140 |
| 0.1015 | 6.0 | 168 | 0.3870 | 0.8372 |
| 0.0767 | 7.0 | 196 | 0.3615 | 0.8837 |
| 0.0522 | 8.0 | 224 | 0.3630 | 0.8837 |
| 0.0344 | 9.0 | 252 | 0.4112 | 0.8837 |
| 0.0303 | 10.0 | 280 | 0.4026 | 0.8837 |
| 0.0199 | 11.0 | 308 | 0.3842 | 0.9070 |
| 0.0106 | 12.0 | 336 | 0.3943 | 0.8605 |
| 0.0205 | 13.0 | 364 | 0.3879 | 0.9070 |
| 0.008 | 14.0 | 392 | 0.3444 | 0.8837 |
| 0.0066 | 15.0 | 420 | 0.3829 | 0.9070 |
| 0.0068 | 16.0 | 448 | 0.4064 | 0.8837 |
| 0.0104 | 17.0 | 476 | 0.3534 | 0.9302 |
| 0.0048 | 18.0 | 504 | 0.3744 | 0.9070 |
| 0.0062 | 19.0 | 532 | 0.4146 | 0.9070 |
| 0.0025 | 20.0 | 560 | 0.3803 | 0.9070 |
| 0.0032 | 21.0 | 588 | 0.4244 | 0.9070 |
| 0.0031 | 22.0 | 616 | 0.4663 | 0.9070 |
| 0.0021 | 23.0 | 644 | 0.4157 | 0.9070 |
| 0.0026 | 24.0 | 672 | 0.4816 | 0.9070 |
| 0.0013 | 25.0 | 700 | 0.4216 | 0.9070 |
| 0.0017 | 26.0 | 728 | 0.4591 | 0.9070 |
| 0.0021 | 27.0 | 756 | 0.4515 | 0.9070 |
| 0.0024 | 28.0 | 784 | 0.4442 | 0.8837 |
| 0.0026 | 29.0 | 812 | 0.4504 | 0.9070 |
| 0.0009 | 30.0 | 840 | 0.4703 | 0.9070 |
| 0.0047 | 31.0 | 868 | 0.4689 | 0.9070 |
| 0.0067 | 32.0 | 896 | 0.4798 | 0.9070 |
| 0.0009 | 33.0 | 924 | 0.5058 | 0.9070 |
| 0.0013 | 34.0 | 952 | 0.4786 | 0.9070 |
| 0.0022 | 35.0 | 980 | 0.4689 | 0.9070 |
| 0.009 | 36.0 | 1008 | 0.4633 | 0.9070 |
| 0.0009 | 37.0 | 1036 | 0.4823 | 0.9070 |
| 0.0013 | 38.0 | 1064 | 0.4868 | 0.9070 |
| 0.0024 | 39.0 | 1092 | 0.5030 | 0.9070 |
| 0.004 | 40.0 | 1120 | 0.4969 | 0.9070 |
| 0.0014 | 41.0 | 1148 | 0.4951 | 0.9070 |
| 0.0017 | 42.0 | 1176 | 0.4894 | 0.9070 |
| 0.0014 | 43.0 | 1204 | 0.4881 | 0.9070 |
| 0.0013 | 44.0 | 1232 | 0.4878 | 0.9070 |
| 0.0022 | 45.0 | 1260 | 0.4914 | 0.9070 |
| 0.0023 | 46.0 | 1288 | 0.4962 | 0.9070 |
| 0.0015 | 47.0 | 1316 | 0.4961 | 0.9070 |
| 0.0017 | 48.0 | 1344 | 0.4974 | 0.9070 |
| 0.0007 | 49.0 | 1372 | 0.4974 | 0.9070 |
| 0.0006 | 50.0 | 1400 | 0.4974 | 0.9070 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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