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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_1x_beit_base_adamax_00001_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: 0.89
---
<!-- 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_1x_beit_base_adamax_00001_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: 0.6882
- Accuracy: 0.89
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3992 | 1.0 | 75 | 0.3544 | 0.845 |
| 0.2938 | 2.0 | 150 | 0.2944 | 0.88 |
| 0.2043 | 3.0 | 225 | 0.2889 | 0.8733 |
| 0.1457 | 4.0 | 300 | 0.2668 | 0.8917 |
| 0.1371 | 5.0 | 375 | 0.2691 | 0.8833 |
| 0.1186 | 6.0 | 450 | 0.2876 | 0.8733 |
| 0.0675 | 7.0 | 525 | 0.2905 | 0.895 |
| 0.0675 | 8.0 | 600 | 0.3070 | 0.8983 |
| 0.0951 | 9.0 | 675 | 0.3449 | 0.8917 |
| 0.0427 | 10.0 | 750 | 0.3642 | 0.885 |
| 0.0217 | 11.0 | 825 | 0.3880 | 0.8817 |
| 0.0513 | 12.0 | 900 | 0.3991 | 0.9 |
| 0.0247 | 13.0 | 975 | 0.4163 | 0.8983 |
| 0.018 | 14.0 | 1050 | 0.4538 | 0.8883 |
| 0.0291 | 15.0 | 1125 | 0.4599 | 0.8917 |
| 0.0096 | 16.0 | 1200 | 0.5126 | 0.89 |
| 0.0106 | 17.0 | 1275 | 0.5125 | 0.8867 |
| 0.0447 | 18.0 | 1350 | 0.5410 | 0.8883 |
| 0.016 | 19.0 | 1425 | 0.5359 | 0.8883 |
| 0.0033 | 20.0 | 1500 | 0.5522 | 0.8867 |
| 0.0086 | 21.0 | 1575 | 0.5579 | 0.8883 |
| 0.0299 | 22.0 | 1650 | 0.5864 | 0.8833 |
| 0.0058 | 23.0 | 1725 | 0.5904 | 0.8867 |
| 0.0156 | 24.0 | 1800 | 0.6102 | 0.89 |
| 0.0161 | 25.0 | 1875 | 0.6210 | 0.8883 |
| 0.0066 | 26.0 | 1950 | 0.6149 | 0.8883 |
| 0.0424 | 27.0 | 2025 | 0.6199 | 0.8867 |
| 0.011 | 28.0 | 2100 | 0.6388 | 0.8867 |
| 0.0021 | 29.0 | 2175 | 0.6358 | 0.8917 |
| 0.0014 | 30.0 | 2250 | 0.6319 | 0.8883 |
| 0.0203 | 31.0 | 2325 | 0.6459 | 0.89 |
| 0.0221 | 32.0 | 2400 | 0.6739 | 0.8883 |
| 0.0066 | 33.0 | 2475 | 0.6562 | 0.89 |
| 0.0119 | 34.0 | 2550 | 0.6704 | 0.885 |
| 0.0088 | 35.0 | 2625 | 0.6526 | 0.89 |
| 0.0115 | 36.0 | 2700 | 0.6534 | 0.8867 |
| 0.0355 | 37.0 | 2775 | 0.6663 | 0.8883 |
| 0.0376 | 38.0 | 2850 | 0.6538 | 0.89 |
| 0.0299 | 39.0 | 2925 | 0.6757 | 0.8867 |
| 0.0019 | 40.0 | 3000 | 0.6764 | 0.8883 |
| 0.0235 | 41.0 | 3075 | 0.6776 | 0.89 |
| 0.0081 | 42.0 | 3150 | 0.6798 | 0.8883 |
| 0.0053 | 43.0 | 3225 | 0.6758 | 0.8883 |
| 0.0234 | 44.0 | 3300 | 0.6788 | 0.8933 |
| 0.0053 | 45.0 | 3375 | 0.6853 | 0.8883 |
| 0.0121 | 46.0 | 3450 | 0.6875 | 0.8867 |
| 0.001 | 47.0 | 3525 | 0.6878 | 0.8883 |
| 0.0104 | 48.0 | 3600 | 0.6872 | 0.89 |
| 0.0042 | 49.0 | 3675 | 0.6870 | 0.8883 |
| 0.0115 | 50.0 | 3750 | 0.6882 | 0.89 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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