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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_001_fold4
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: 0.8533333333333334
---
<!-- 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_001_fold4
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: 1.5511
- Accuracy: 0.8533
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4815 | 1.0 | 225 | 0.6186 | 0.7683 |
| 0.3544 | 2.0 | 450 | 0.5568 | 0.81 |
| 0.3541 | 3.0 | 675 | 0.4838 | 0.8283 |
| 0.3428 | 4.0 | 900 | 0.5170 | 0.8317 |
| 0.2833 | 5.0 | 1125 | 0.5822 | 0.7917 |
| 0.2012 | 6.0 | 1350 | 0.4801 | 0.8333 |
| 0.1747 | 7.0 | 1575 | 0.5354 | 0.8283 |
| 0.244 | 8.0 | 1800 | 0.5484 | 0.8467 |
| 0.2186 | 9.0 | 2025 | 0.6193 | 0.8167 |
| 0.155 | 10.0 | 2250 | 0.5230 | 0.8483 |
| 0.088 | 11.0 | 2475 | 0.6574 | 0.8367 |
| 0.1048 | 12.0 | 2700 | 0.5983 | 0.8517 |
| 0.0758 | 13.0 | 2925 | 0.7255 | 0.835 |
| 0.0236 | 14.0 | 3150 | 0.7448 | 0.835 |
| 0.088 | 15.0 | 3375 | 0.9152 | 0.84 |
| 0.0685 | 16.0 | 3600 | 0.9354 | 0.8167 |
| 0.0326 | 17.0 | 3825 | 0.9022 | 0.8417 |
| 0.0167 | 18.0 | 4050 | 0.9397 | 0.8417 |
| 0.0222 | 19.0 | 4275 | 0.9135 | 0.8433 |
| 0.0409 | 20.0 | 4500 | 1.1656 | 0.8467 |
| 0.0192 | 21.0 | 4725 | 1.0036 | 0.8617 |
| 0.0016 | 22.0 | 4950 | 0.9822 | 0.8467 |
| 0.0233 | 23.0 | 5175 | 0.9225 | 0.845 |
| 0.0233 | 24.0 | 5400 | 1.0565 | 0.8483 |
| 0.0482 | 25.0 | 5625 | 1.2063 | 0.835 |
| 0.005 | 26.0 | 5850 | 1.0210 | 0.8467 |
| 0.0014 | 27.0 | 6075 | 1.0482 | 0.8517 |
| 0.0002 | 28.0 | 6300 | 1.2044 | 0.8383 |
| 0.0037 | 29.0 | 6525 | 1.0861 | 0.8533 |
| 0.0174 | 30.0 | 6750 | 1.1444 | 0.85 |
| 0.0 | 31.0 | 6975 | 1.2721 | 0.8567 |
| 0.0109 | 32.0 | 7200 | 1.2042 | 0.8583 |
| 0.0095 | 33.0 | 7425 | 1.1796 | 0.8517 |
| 0.0112 | 34.0 | 7650 | 1.2147 | 0.8583 |
| 0.0005 | 35.0 | 7875 | 1.2717 | 0.85 |
| 0.0001 | 36.0 | 8100 | 1.3234 | 0.8517 |
| 0.0015 | 37.0 | 8325 | 1.3845 | 0.8567 |
| 0.0007 | 38.0 | 8550 | 1.3111 | 0.8583 |
| 0.0 | 39.0 | 8775 | 1.3423 | 0.8567 |
| 0.0 | 40.0 | 9000 | 1.3863 | 0.855 |
| 0.0 | 41.0 | 9225 | 1.3890 | 0.8567 |
| 0.0025 | 42.0 | 9450 | 1.5279 | 0.855 |
| 0.0 | 43.0 | 9675 | 1.5233 | 0.855 |
| 0.0023 | 44.0 | 9900 | 1.5389 | 0.8567 |
| 0.0 | 45.0 | 10125 | 1.5451 | 0.8517 |
| 0.0 | 46.0 | 10350 | 1.5273 | 0.8517 |
| 0.0 | 47.0 | 10575 | 1.5407 | 0.8517 |
| 0.0 | 48.0 | 10800 | 1.5468 | 0.8517 |
| 0.0 | 49.0 | 11025 | 1.5542 | 0.8533 |
| 0.0 | 50.0 | 11250 | 1.5511 | 0.8533 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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