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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_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: 0.8816666666666667
---
<!-- 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_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: 1.1371
- Accuracy: 0.8817
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4638 | 1.0 | 225 | 0.4831 | 0.8 |
| 0.3593 | 2.0 | 450 | 0.4972 | 0.8017 |
| 0.3188 | 3.0 | 675 | 0.4598 | 0.8167 |
| 0.3007 | 4.0 | 900 | 0.3652 | 0.8567 |
| 0.1782 | 5.0 | 1125 | 0.4943 | 0.8467 |
| 0.2023 | 6.0 | 1350 | 0.3456 | 0.8833 |
| 0.1823 | 7.0 | 1575 | 0.4718 | 0.8517 |
| 0.2135 | 8.0 | 1800 | 0.4289 | 0.8333 |
| 0.1901 | 9.0 | 2025 | 0.3868 | 0.8767 |
| 0.1186 | 10.0 | 2250 | 0.5005 | 0.8567 |
| 0.1591 | 11.0 | 2475 | 0.4399 | 0.8633 |
| 0.1322 | 12.0 | 2700 | 0.4503 | 0.88 |
| 0.1689 | 13.0 | 2925 | 0.5822 | 0.855 |
| 0.1287 | 14.0 | 3150 | 0.5651 | 0.8533 |
| 0.0522 | 15.0 | 3375 | 0.6382 | 0.875 |
| 0.0395 | 16.0 | 3600 | 0.6522 | 0.88 |
| 0.0429 | 17.0 | 3825 | 0.6980 | 0.875 |
| 0.0661 | 18.0 | 4050 | 0.7096 | 0.855 |
| 0.0192 | 19.0 | 4275 | 0.7298 | 0.8733 |
| 0.0389 | 20.0 | 4500 | 0.7458 | 0.875 |
| 0.0026 | 21.0 | 4725 | 0.7349 | 0.88 |
| 0.0037 | 22.0 | 4950 | 0.8178 | 0.8833 |
| 0.006 | 23.0 | 5175 | 0.9683 | 0.8667 |
| 0.0578 | 24.0 | 5400 | 0.7875 | 0.8817 |
| 0.049 | 25.0 | 5625 | 0.7063 | 0.87 |
| 0.0011 | 26.0 | 5850 | 0.8220 | 0.8717 |
| 0.0006 | 27.0 | 6075 | 0.7462 | 0.8833 |
| 0.0152 | 28.0 | 6300 | 0.8411 | 0.8817 |
| 0.0204 | 29.0 | 6525 | 0.9258 | 0.875 |
| 0.0002 | 30.0 | 6750 | 0.8705 | 0.8683 |
| 0.0004 | 31.0 | 6975 | 0.8382 | 0.88 |
| 0.0001 | 32.0 | 7200 | 0.8871 | 0.8767 |
| 0.0069 | 33.0 | 7425 | 0.6754 | 0.8983 |
| 0.0062 | 34.0 | 7650 | 0.7823 | 0.8983 |
| 0.003 | 35.0 | 7875 | 0.8358 | 0.8883 |
| 0.0 | 36.0 | 8100 | 0.9463 | 0.885 |
| 0.0144 | 37.0 | 8325 | 1.0937 | 0.8717 |
| 0.0078 | 38.0 | 8550 | 1.0295 | 0.8867 |
| 0.0 | 39.0 | 8775 | 1.0240 | 0.8883 |
| 0.0001 | 40.0 | 9000 | 1.0443 | 0.8833 |
| 0.0 | 41.0 | 9225 | 1.0675 | 0.8933 |
| 0.0 | 42.0 | 9450 | 1.1657 | 0.8767 |
| 0.0035 | 43.0 | 9675 | 1.1312 | 0.8717 |
| 0.0 | 44.0 | 9900 | 1.1156 | 0.885 |
| 0.0 | 45.0 | 10125 | 1.1160 | 0.8817 |
| 0.0 | 46.0 | 10350 | 1.1325 | 0.8833 |
| 0.0008 | 47.0 | 10575 | 1.1407 | 0.8817 |
| 0.0 | 48.0 | 10800 | 1.1472 | 0.88 |
| 0.0001 | 49.0 | 11025 | 1.1387 | 0.8817 |
| 0.0 | 50.0 | 11250 | 1.1371 | 0.8817 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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