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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_fold1
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: 0.8998330550918197
---
<!-- 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_fold1
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: 0.8235
- Accuracy: 0.8998
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3449 | 1.0 | 226 | 0.2404 | 0.8982 |
| 0.164 | 2.0 | 452 | 0.2594 | 0.9098 |
| 0.0958 | 3.0 | 678 | 0.3903 | 0.8715 |
| 0.1173 | 4.0 | 904 | 0.3785 | 0.8982 |
| 0.071 | 5.0 | 1130 | 0.3702 | 0.9032 |
| 0.0146 | 6.0 | 1356 | 0.5160 | 0.8932 |
| 0.004 | 7.0 | 1582 | 0.5036 | 0.8965 |
| 0.0021 | 8.0 | 1808 | 0.5998 | 0.9015 |
| 0.0217 | 9.0 | 2034 | 0.6221 | 0.8998 |
| 0.0119 | 10.0 | 2260 | 0.6511 | 0.9082 |
| 0.0029 | 11.0 | 2486 | 0.6550 | 0.8932 |
| 0.0209 | 12.0 | 2712 | 0.5564 | 0.9082 |
| 0.0119 | 13.0 | 2938 | 0.7071 | 0.9015 |
| 0.0109 | 14.0 | 3164 | 0.6721 | 0.8965 |
| 0.0179 | 15.0 | 3390 | 0.6523 | 0.8965 |
| 0.0016 | 16.0 | 3616 | 0.6369 | 0.9149 |
| 0.0197 | 17.0 | 3842 | 0.8098 | 0.8932 |
| 0.0022 | 18.0 | 4068 | 0.7112 | 0.8948 |
| 0.017 | 19.0 | 4294 | 0.8580 | 0.8898 |
| 0.0002 | 20.0 | 4520 | 0.8600 | 0.8915 |
| 0.014 | 21.0 | 4746 | 0.8484 | 0.8932 |
| 0.0155 | 22.0 | 4972 | 0.7756 | 0.8932 |
| 0.0055 | 23.0 | 5198 | 0.7307 | 0.9082 |
| 0.016 | 24.0 | 5424 | 0.7520 | 0.9065 |
| 0.0 | 25.0 | 5650 | 0.6900 | 0.9165 |
| 0.0009 | 26.0 | 5876 | 0.7482 | 0.8998 |
| 0.0 | 27.0 | 6102 | 0.6921 | 0.9032 |
| 0.0022 | 28.0 | 6328 | 0.6800 | 0.9098 |
| 0.0 | 29.0 | 6554 | 0.6295 | 0.9215 |
| 0.0004 | 30.0 | 6780 | 0.6201 | 0.9182 |
| 0.0 | 31.0 | 7006 | 0.6546 | 0.9182 |
| 0.0 | 32.0 | 7232 | 0.6675 | 0.9098 |
| 0.0055 | 33.0 | 7458 | 0.7721 | 0.9048 |
| 0.0014 | 34.0 | 7684 | 0.8129 | 0.8965 |
| 0.0 | 35.0 | 7910 | 0.8045 | 0.8998 |
| 0.0 | 36.0 | 8136 | 0.7737 | 0.8998 |
| 0.0005 | 37.0 | 8362 | 0.7575 | 0.9065 |
| 0.0 | 38.0 | 8588 | 0.7935 | 0.9098 |
| 0.0001 | 39.0 | 8814 | 0.8075 | 0.8948 |
| 0.0 | 40.0 | 9040 | 0.7870 | 0.9065 |
| 0.0033 | 41.0 | 9266 | 0.7830 | 0.8965 |
| 0.0059 | 42.0 | 9492 | 0.8352 | 0.9015 |
| 0.0 | 43.0 | 9718 | 0.7765 | 0.9098 |
| 0.0 | 44.0 | 9944 | 0.8178 | 0.9065 |
| 0.0123 | 45.0 | 10170 | 0.8336 | 0.8982 |
| 0.0 | 46.0 | 10396 | 0.8075 | 0.9048 |
| 0.0 | 47.0 | 10622 | 0.8113 | 0.9048 |
| 0.0 | 48.0 | 10848 | 0.8125 | 0.9032 |
| 0.0 | 49.0 | 11074 | 0.8258 | 0.8982 |
| 0.0 | 50.0 | 11300 | 0.8235 | 0.8998 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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