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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_rms_00001_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: 0.8833333333333333
---
<!-- 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_rms_00001_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: 1.2428
- Accuracy: 0.8833
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2439 | 1.0 | 225 | 0.3063 | 0.88 |
| 0.0996 | 2.0 | 450 | 0.3661 | 0.8767 |
| 0.0787 | 3.0 | 675 | 0.4587 | 0.8667 |
| 0.0663 | 4.0 | 900 | 0.5189 | 0.8733 |
| 0.0442 | 5.0 | 1125 | 0.7230 | 0.8683 |
| 0.049 | 6.0 | 1350 | 0.6529 | 0.885 |
| 0.0264 | 7.0 | 1575 | 0.8061 | 0.8817 |
| 0.0229 | 8.0 | 1800 | 0.7781 | 0.89 |
| 0.0395 | 9.0 | 2025 | 0.9069 | 0.88 |
| 0.0034 | 10.0 | 2250 | 0.8845 | 0.885 |
| 0.0389 | 11.0 | 2475 | 1.0336 | 0.8783 |
| 0.0025 | 12.0 | 2700 | 0.9857 | 0.8867 |
| 0.0142 | 13.0 | 2925 | 1.0341 | 0.885 |
| 0.0196 | 14.0 | 3150 | 1.1721 | 0.8767 |
| 0.0094 | 15.0 | 3375 | 1.0615 | 0.8767 |
| 0.0053 | 16.0 | 3600 | 1.1359 | 0.8767 |
| 0.0019 | 17.0 | 3825 | 1.1838 | 0.88 |
| 0.0236 | 18.0 | 4050 | 1.3731 | 0.8617 |
| 0.0037 | 19.0 | 4275 | 1.2473 | 0.8683 |
| 0.0001 | 20.0 | 4500 | 1.1836 | 0.8833 |
| 0.0008 | 21.0 | 4725 | 1.2284 | 0.8733 |
| 0.0 | 22.0 | 4950 | 1.1971 | 0.8867 |
| 0.015 | 23.0 | 5175 | 1.2985 | 0.8783 |
| 0.0 | 24.0 | 5400 | 1.3191 | 0.8683 |
| 0.0386 | 25.0 | 5625 | 1.3376 | 0.88 |
| 0.0001 | 26.0 | 5850 | 1.3273 | 0.8717 |
| 0.0019 | 27.0 | 6075 | 1.3269 | 0.8683 |
| 0.0001 | 28.0 | 6300 | 1.3093 | 0.8733 |
| 0.0 | 29.0 | 6525 | 1.2247 | 0.88 |
| 0.0 | 30.0 | 6750 | 1.2682 | 0.8733 |
| 0.0 | 31.0 | 6975 | 1.2123 | 0.8833 |
| 0.0 | 32.0 | 7200 | 1.2162 | 0.885 |
| 0.0027 | 33.0 | 7425 | 1.2786 | 0.8783 |
| 0.0 | 34.0 | 7650 | 1.3256 | 0.8817 |
| 0.0286 | 35.0 | 7875 | 1.2152 | 0.89 |
| 0.0 | 36.0 | 8100 | 1.2207 | 0.8833 |
| 0.0001 | 37.0 | 8325 | 1.2285 | 0.885 |
| 0.0004 | 38.0 | 8550 | 1.1956 | 0.89 |
| 0.0 | 39.0 | 8775 | 1.1853 | 0.8867 |
| 0.0401 | 40.0 | 9000 | 1.1341 | 0.8967 |
| 0.0 | 41.0 | 9225 | 1.1526 | 0.8883 |
| 0.0032 | 42.0 | 9450 | 1.1907 | 0.8817 |
| 0.0002 | 43.0 | 9675 | 1.2154 | 0.8833 |
| 0.0022 | 44.0 | 9900 | 1.1934 | 0.8833 |
| 0.0 | 45.0 | 10125 | 1.2765 | 0.88 |
| 0.0 | 46.0 | 10350 | 1.2545 | 0.8767 |
| 0.0002 | 47.0 | 10575 | 1.2393 | 0.8817 |
| 0.0 | 48.0 | 10800 | 1.2475 | 0.8817 |
| 0.0 | 49.0 | 11025 | 1.2453 | 0.8817 |
| 0.0 | 50.0 | 11250 | 1.2428 | 0.8833 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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