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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: smids_3x_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: 0.9066666666666666
---
<!-- 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_3x_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: 0.8012
- Accuracy: 0.9067
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3558 | 1.0 | 225 | 0.2857 | 0.8817 |
| 0.2025 | 2.0 | 450 | 0.2548 | 0.9083 |
| 0.1598 | 3.0 | 675 | 0.2521 | 0.92 |
| 0.1219 | 4.0 | 900 | 0.2685 | 0.9067 |
| 0.1177 | 5.0 | 1125 | 0.2855 | 0.9167 |
| 0.0821 | 6.0 | 1350 | 0.3265 | 0.915 |
| 0.035 | 7.0 | 1575 | 0.3390 | 0.9133 |
| 0.0488 | 8.0 | 1800 | 0.3876 | 0.91 |
| 0.0333 | 9.0 | 2025 | 0.4069 | 0.9183 |
| 0.0137 | 10.0 | 2250 | 0.4823 | 0.895 |
| 0.0425 | 11.0 | 2475 | 0.4830 | 0.91 |
| 0.0131 | 12.0 | 2700 | 0.5278 | 0.9067 |
| 0.0362 | 13.0 | 2925 | 0.5365 | 0.91 |
| 0.0127 | 14.0 | 3150 | 0.5604 | 0.91 |
| 0.0059 | 15.0 | 3375 | 0.5988 | 0.9067 |
| 0.0457 | 16.0 | 3600 | 0.6291 | 0.8983 |
| 0.0096 | 17.0 | 3825 | 0.6121 | 0.905 |
| 0.0291 | 18.0 | 4050 | 0.6425 | 0.91 |
| 0.0279 | 19.0 | 4275 | 0.6328 | 0.9017 |
| 0.006 | 20.0 | 4500 | 0.7129 | 0.905 |
| 0.0195 | 21.0 | 4725 | 0.7320 | 0.9017 |
| 0.0002 | 22.0 | 4950 | 0.7512 | 0.9017 |
| 0.0352 | 23.0 | 5175 | 0.7248 | 0.9067 |
| 0.0032 | 24.0 | 5400 | 0.7414 | 0.9 |
| 0.0649 | 25.0 | 5625 | 0.7106 | 0.915 |
| 0.0454 | 26.0 | 5850 | 0.7165 | 0.91 |
| 0.0011 | 27.0 | 6075 | 0.7232 | 0.915 |
| 0.0041 | 28.0 | 6300 | 0.7095 | 0.9117 |
| 0.0099 | 29.0 | 6525 | 0.7308 | 0.9083 |
| 0.0129 | 30.0 | 6750 | 0.7895 | 0.9083 |
| 0.0212 | 31.0 | 6975 | 0.7650 | 0.91 |
| 0.0018 | 32.0 | 7200 | 0.7684 | 0.9083 |
| 0.0006 | 33.0 | 7425 | 0.7607 | 0.9133 |
| 0.0001 | 34.0 | 7650 | 0.7555 | 0.9117 |
| 0.0002 | 35.0 | 7875 | 0.7851 | 0.9083 |
| 0.0002 | 36.0 | 8100 | 0.7601 | 0.9117 |
| 0.0002 | 37.0 | 8325 | 0.7878 | 0.9083 |
| 0.0284 | 38.0 | 8550 | 0.7877 | 0.9083 |
| 0.0007 | 39.0 | 8775 | 0.7993 | 0.9067 |
| 0.002 | 40.0 | 9000 | 0.7969 | 0.91 |
| 0.0004 | 41.0 | 9225 | 0.8163 | 0.9083 |
| 0.0234 | 42.0 | 9450 | 0.7871 | 0.915 |
| 0.0006 | 43.0 | 9675 | 0.8006 | 0.9067 |
| 0.0004 | 44.0 | 9900 | 0.7989 | 0.9083 |
| 0.0007 | 45.0 | 10125 | 0.8058 | 0.9067 |
| 0.0174 | 46.0 | 10350 | 0.8151 | 0.9017 |
| 0.0003 | 47.0 | 10575 | 0.8093 | 0.9033 |
| 0.0 | 48.0 | 10800 | 0.8021 | 0.9067 |
| 0.0012 | 49.0 | 11025 | 0.8063 | 0.9067 |
| 0.0009 | 50.0 | 11250 | 0.8012 | 0.9067 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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