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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_sgd_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: 0.8783333333333333
---
<!-- 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_sgd_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: 0.3105
- Accuracy: 0.8783
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8516 | 1.0 | 225 | 0.8297 | 0.6267 |
| 0.6679 | 2.0 | 450 | 0.6103 | 0.7567 |
| 0.57 | 3.0 | 675 | 0.5223 | 0.7883 |
| 0.4959 | 4.0 | 900 | 0.4753 | 0.8083 |
| 0.4424 | 5.0 | 1125 | 0.4319 | 0.8233 |
| 0.4261 | 6.0 | 1350 | 0.4129 | 0.8283 |
| 0.4396 | 7.0 | 1575 | 0.4075 | 0.8167 |
| 0.4595 | 8.0 | 1800 | 0.3942 | 0.8267 |
| 0.4172 | 9.0 | 2025 | 0.3692 | 0.8367 |
| 0.3688 | 10.0 | 2250 | 0.3605 | 0.8583 |
| 0.4132 | 11.0 | 2475 | 0.3610 | 0.8417 |
| 0.369 | 12.0 | 2700 | 0.3465 | 0.8567 |
| 0.3672 | 13.0 | 2925 | 0.3443 | 0.8517 |
| 0.3409 | 14.0 | 3150 | 0.3437 | 0.855 |
| 0.2695 | 15.0 | 3375 | 0.3370 | 0.8567 |
| 0.311 | 16.0 | 3600 | 0.3373 | 0.8533 |
| 0.3177 | 17.0 | 3825 | 0.3325 | 0.8567 |
| 0.3059 | 18.0 | 4050 | 0.3310 | 0.8567 |
| 0.3295 | 19.0 | 4275 | 0.3271 | 0.8583 |
| 0.3201 | 20.0 | 4500 | 0.3301 | 0.8667 |
| 0.2645 | 21.0 | 4725 | 0.3242 | 0.8683 |
| 0.2497 | 22.0 | 4950 | 0.3240 | 0.8633 |
| 0.2626 | 23.0 | 5175 | 0.3196 | 0.8617 |
| 0.267 | 24.0 | 5400 | 0.3185 | 0.8733 |
| 0.2637 | 25.0 | 5625 | 0.3155 | 0.8733 |
| 0.3416 | 26.0 | 5850 | 0.3155 | 0.8783 |
| 0.3255 | 27.0 | 6075 | 0.3159 | 0.8767 |
| 0.3021 | 28.0 | 6300 | 0.3189 | 0.875 |
| 0.2292 | 29.0 | 6525 | 0.3137 | 0.8783 |
| 0.2207 | 30.0 | 6750 | 0.3185 | 0.8733 |
| 0.2158 | 31.0 | 6975 | 0.3173 | 0.8683 |
| 0.2149 | 32.0 | 7200 | 0.3154 | 0.87 |
| 0.248 | 33.0 | 7425 | 0.3134 | 0.8767 |
| 0.2339 | 34.0 | 7650 | 0.3133 | 0.875 |
| 0.2585 | 35.0 | 7875 | 0.3147 | 0.8767 |
| 0.2565 | 36.0 | 8100 | 0.3120 | 0.875 |
| 0.269 | 37.0 | 8325 | 0.3111 | 0.8783 |
| 0.2546 | 38.0 | 8550 | 0.3139 | 0.8733 |
| 0.2114 | 39.0 | 8775 | 0.3110 | 0.8767 |
| 0.2032 | 40.0 | 9000 | 0.3108 | 0.8767 |
| 0.2376 | 41.0 | 9225 | 0.3108 | 0.8783 |
| 0.2558 | 42.0 | 9450 | 0.3092 | 0.8767 |
| 0.2753 | 43.0 | 9675 | 0.3113 | 0.875 |
| 0.2795 | 44.0 | 9900 | 0.3109 | 0.8767 |
| 0.2412 | 45.0 | 10125 | 0.3113 | 0.8783 |
| 0.2003 | 46.0 | 10350 | 0.3105 | 0.88 |
| 0.2528 | 47.0 | 10575 | 0.3109 | 0.88 |
| 0.2265 | 48.0 | 10800 | 0.3109 | 0.8783 |
| 0.2494 | 49.0 | 11025 | 0.3106 | 0.8783 |
| 0.2763 | 50.0 | 11250 | 0.3105 | 0.8783 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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