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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_5x_beit_base_adamax_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: 0.9065108514190318
---
<!-- 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_5x_beit_base_adamax_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: 0.8312
- Accuracy: 0.9065
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2349 | 1.0 | 376 | 0.2964 | 0.8848 |
| 0.2022 | 2.0 | 752 | 0.2944 | 0.8932 |
| 0.1706 | 3.0 | 1128 | 0.2893 | 0.8965 |
| 0.0767 | 4.0 | 1504 | 0.3105 | 0.9015 |
| 0.0646 | 5.0 | 1880 | 0.3471 | 0.9015 |
| 0.0505 | 6.0 | 2256 | 0.3777 | 0.9015 |
| 0.0505 | 7.0 | 2632 | 0.4146 | 0.9115 |
| 0.0821 | 8.0 | 3008 | 0.4739 | 0.9115 |
| 0.0331 | 9.0 | 3384 | 0.5133 | 0.9082 |
| 0.0097 | 10.0 | 3760 | 0.5125 | 0.9065 |
| 0.0368 | 11.0 | 4136 | 0.5327 | 0.9098 |
| 0.0236 | 12.0 | 4512 | 0.6377 | 0.8881 |
| 0.0306 | 13.0 | 4888 | 0.6671 | 0.9015 |
| 0.0605 | 14.0 | 5264 | 0.6154 | 0.9048 |
| 0.0306 | 15.0 | 5640 | 0.6497 | 0.9082 |
| 0.0004 | 16.0 | 6016 | 0.6905 | 0.9098 |
| 0.0062 | 17.0 | 6392 | 0.7456 | 0.9082 |
| 0.0157 | 18.0 | 6768 | 0.7362 | 0.9048 |
| 0.0117 | 19.0 | 7144 | 0.8082 | 0.8965 |
| 0.0001 | 20.0 | 7520 | 0.7613 | 0.9098 |
| 0.0049 | 21.0 | 7896 | 0.7376 | 0.9115 |
| 0.0013 | 22.0 | 8272 | 0.7490 | 0.9098 |
| 0.0339 | 23.0 | 8648 | 0.7577 | 0.9132 |
| 0.0009 | 24.0 | 9024 | 0.7847 | 0.9098 |
| 0.0161 | 25.0 | 9400 | 0.7983 | 0.9098 |
| 0.0079 | 26.0 | 9776 | 0.7734 | 0.8948 |
| 0.0004 | 27.0 | 10152 | 0.7368 | 0.9015 |
| 0.0005 | 28.0 | 10528 | 0.7478 | 0.9098 |
| 0.0059 | 29.0 | 10904 | 0.7755 | 0.9065 |
| 0.0012 | 30.0 | 11280 | 0.8338 | 0.9082 |
| 0.0142 | 31.0 | 11656 | 0.7783 | 0.9115 |
| 0.0002 | 32.0 | 12032 | 0.7615 | 0.9165 |
| 0.0004 | 33.0 | 12408 | 0.7711 | 0.9098 |
| 0.0127 | 34.0 | 12784 | 0.7865 | 0.9165 |
| 0.0032 | 35.0 | 13160 | 0.8207 | 0.9132 |
| 0.0006 | 36.0 | 13536 | 0.8174 | 0.9098 |
| 0.0001 | 37.0 | 13912 | 0.7992 | 0.9165 |
| 0.0 | 38.0 | 14288 | 0.8040 | 0.9082 |
| 0.0001 | 39.0 | 14664 | 0.8011 | 0.9132 |
| 0.0005 | 40.0 | 15040 | 0.8052 | 0.9115 |
| 0.0001 | 41.0 | 15416 | 0.8158 | 0.9082 |
| 0.0001 | 42.0 | 15792 | 0.8157 | 0.9098 |
| 0.0 | 43.0 | 16168 | 0.8347 | 0.9065 |
| 0.0004 | 44.0 | 16544 | 0.8096 | 0.9048 |
| 0.0087 | 45.0 | 16920 | 0.8231 | 0.9065 |
| 0.0003 | 46.0 | 17296 | 0.8362 | 0.9065 |
| 0.0002 | 47.0 | 17672 | 0.8291 | 0.9098 |
| 0.0046 | 48.0 | 18048 | 0.8341 | 0.9082 |
| 0.0134 | 49.0 | 18424 | 0.8309 | 0.9065 |
| 0.0004 | 50.0 | 18800 | 0.8312 | 0.9065 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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