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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_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: 0.7633333333333333
---
<!-- 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_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: 2.1833
- Accuracy: 0.7633
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9804 | 1.0 | 75 | 0.8561 | 0.5383 |
| 0.8823 | 2.0 | 150 | 0.7905 | 0.5767 |
| 0.8002 | 3.0 | 225 | 0.7961 | 0.5633 |
| 0.8142 | 4.0 | 300 | 0.8679 | 0.6133 |
| 0.6765 | 5.0 | 375 | 0.6964 | 0.6817 |
| 0.652 | 6.0 | 450 | 0.6686 | 0.7 |
| 0.6785 | 7.0 | 525 | 0.6625 | 0.7067 |
| 0.5659 | 8.0 | 600 | 0.6154 | 0.7217 |
| 0.6383 | 9.0 | 675 | 0.6262 | 0.7117 |
| 0.5991 | 10.0 | 750 | 0.5856 | 0.7633 |
| 0.4627 | 11.0 | 825 | 0.5901 | 0.7633 |
| 0.5021 | 12.0 | 900 | 0.5968 | 0.7433 |
| 0.5421 | 13.0 | 975 | 0.5857 | 0.74 |
| 0.3951 | 14.0 | 1050 | 0.5723 | 0.7733 |
| 0.4943 | 15.0 | 1125 | 0.6046 | 0.7533 |
| 0.4076 | 16.0 | 1200 | 0.6196 | 0.7567 |
| 0.379 | 17.0 | 1275 | 0.5906 | 0.7817 |
| 0.3759 | 18.0 | 1350 | 0.5998 | 0.775 |
| 0.3383 | 19.0 | 1425 | 0.6508 | 0.7567 |
| 0.2622 | 20.0 | 1500 | 0.6675 | 0.775 |
| 0.316 | 21.0 | 1575 | 0.7118 | 0.785 |
| 0.2478 | 22.0 | 1650 | 0.7508 | 0.78 |
| 0.2696 | 23.0 | 1725 | 0.7052 | 0.7733 |
| 0.1441 | 24.0 | 1800 | 0.8658 | 0.7783 |
| 0.1966 | 25.0 | 1875 | 0.9393 | 0.7417 |
| 0.1228 | 26.0 | 1950 | 1.0783 | 0.7567 |
| 0.2151 | 27.0 | 2025 | 1.0051 | 0.7533 |
| 0.1799 | 28.0 | 2100 | 1.0898 | 0.755 |
| 0.1053 | 29.0 | 2175 | 1.0567 | 0.7533 |
| 0.122 | 30.0 | 2250 | 1.1544 | 0.7583 |
| 0.1375 | 31.0 | 2325 | 1.3014 | 0.7617 |
| 0.0659 | 32.0 | 2400 | 1.6359 | 0.765 |
| 0.0997 | 33.0 | 2475 | 1.4213 | 0.7717 |
| 0.0852 | 34.0 | 2550 | 1.6657 | 0.7467 |
| 0.0752 | 35.0 | 2625 | 1.5943 | 0.7733 |
| 0.0405 | 36.0 | 2700 | 1.5865 | 0.7583 |
| 0.0174 | 37.0 | 2775 | 1.8002 | 0.7533 |
| 0.0364 | 38.0 | 2850 | 1.6078 | 0.7583 |
| 0.0269 | 39.0 | 2925 | 2.0543 | 0.7667 |
| 0.0034 | 40.0 | 3000 | 2.1698 | 0.7517 |
| 0.0428 | 41.0 | 3075 | 1.8011 | 0.74 |
| 0.0355 | 42.0 | 3150 | 2.1588 | 0.7567 |
| 0.0068 | 43.0 | 3225 | 2.0789 | 0.7617 |
| 0.013 | 44.0 | 3300 | 2.0235 | 0.76 |
| 0.0102 | 45.0 | 3375 | 1.9567 | 0.7567 |
| 0.0216 | 46.0 | 3450 | 1.9788 | 0.765 |
| 0.0016 | 47.0 | 3525 | 2.1056 | 0.765 |
| 0.0046 | 48.0 | 3600 | 2.1156 | 0.7633 |
| 0.0115 | 49.0 | 3675 | 2.2014 | 0.7617 |
| 0.0156 | 50.0 | 3750 | 2.1833 | 0.7633 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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