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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_adamax_001_fold3
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_adamax_001_fold3
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: 0.9113
- 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7164 | 1.0 | 225 | 0.4684 | 0.81 |
| 0.3974 | 2.0 | 450 | 0.3760 | 0.845 |
| 0.3042 | 3.0 | 675 | 0.4562 | 0.82 |
| 0.264 | 4.0 | 900 | 0.3521 | 0.86 |
| 0.2635 | 5.0 | 1125 | 0.3585 | 0.8567 |
| 0.3122 | 6.0 | 1350 | 0.3482 | 0.87 |
| 0.1881 | 7.0 | 1575 | 0.4250 | 0.8583 |
| 0.2288 | 8.0 | 1800 | 0.4228 | 0.8583 |
| 0.1644 | 9.0 | 2025 | 0.5487 | 0.8367 |
| 0.1666 | 10.0 | 2250 | 0.4820 | 0.8467 |
| 0.1186 | 11.0 | 2475 | 0.6337 | 0.835 |
| 0.1307 | 12.0 | 2700 | 0.4076 | 0.87 |
| 0.0842 | 13.0 | 2925 | 0.5631 | 0.8733 |
| 0.0933 | 14.0 | 3150 | 0.5566 | 0.8767 |
| 0.0383 | 15.0 | 3375 | 0.6882 | 0.8433 |
| 0.0107 | 16.0 | 3600 | 0.5512 | 0.87 |
| 0.0331 | 17.0 | 3825 | 0.5868 | 0.8617 |
| 0.0654 | 18.0 | 4050 | 0.7675 | 0.8517 |
| 0.0588 | 19.0 | 4275 | 0.5953 | 0.8833 |
| 0.0197 | 20.0 | 4500 | 0.6863 | 0.875 |
| 0.0147 | 21.0 | 4725 | 0.7719 | 0.8717 |
| 0.0638 | 22.0 | 4950 | 0.7585 | 0.87 |
| 0.0213 | 23.0 | 5175 | 0.7631 | 0.8667 |
| 0.0027 | 24.0 | 5400 | 0.8123 | 0.8717 |
| 0.0619 | 25.0 | 5625 | 0.6777 | 0.87 |
| 0.0044 | 26.0 | 5850 | 0.7468 | 0.8833 |
| 0.0118 | 27.0 | 6075 | 0.7959 | 0.8683 |
| 0.0014 | 28.0 | 6300 | 0.6725 | 0.8733 |
| 0.0196 | 29.0 | 6525 | 0.8072 | 0.8733 |
| 0.0092 | 30.0 | 6750 | 0.7937 | 0.8833 |
| 0.0065 | 31.0 | 6975 | 0.9261 | 0.875 |
| 0.0008 | 32.0 | 7200 | 0.8949 | 0.875 |
| 0.0001 | 33.0 | 7425 | 0.8856 | 0.89 |
| 0.0027 | 34.0 | 7650 | 0.8960 | 0.8633 |
| 0.0 | 35.0 | 7875 | 0.9060 | 0.87 |
| 0.0 | 36.0 | 8100 | 0.8882 | 0.875 |
| 0.0044 | 37.0 | 8325 | 0.9127 | 0.8783 |
| 0.0 | 38.0 | 8550 | 0.9987 | 0.8767 |
| 0.0 | 39.0 | 8775 | 0.9306 | 0.8817 |
| 0.0 | 40.0 | 9000 | 0.8606 | 0.885 |
| 0.0 | 41.0 | 9225 | 0.8647 | 0.8817 |
| 0.0 | 42.0 | 9450 | 0.8530 | 0.88 |
| 0.0 | 43.0 | 9675 | 0.8745 | 0.885 |
| 0.0 | 44.0 | 9900 | 0.8799 | 0.8817 |
| 0.0 | 45.0 | 10125 | 0.9191 | 0.87 |
| 0.0 | 46.0 | 10350 | 0.9238 | 0.88 |
| 0.0033 | 47.0 | 10575 | 0.9260 | 0.8783 |
| 0.0 | 48.0 | 10800 | 0.9161 | 0.8767 |
| 0.0 | 49.0 | 11025 | 0.9134 | 0.88 |
| 0.0 | 50.0 | 11250 | 0.9113 | 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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