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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_sgd_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: 0.40166666666666667
---
<!-- 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_sgd_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: 1.1301
- Accuracy: 0.4017
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2159 | 1.0 | 375 | 1.3104 | 0.3133 |
| 1.2415 | 2.0 | 750 | 1.3020 | 0.3233 |
| 1.2057 | 3.0 | 1125 | 1.2939 | 0.3233 |
| 1.176 | 4.0 | 1500 | 1.2863 | 0.3267 |
| 1.2191 | 5.0 | 1875 | 1.2790 | 0.3267 |
| 1.1863 | 6.0 | 2250 | 1.2719 | 0.3333 |
| 1.2037 | 7.0 | 2625 | 1.2651 | 0.34 |
| 1.177 | 8.0 | 3000 | 1.2586 | 0.3483 |
| 1.1576 | 9.0 | 3375 | 1.2521 | 0.35 |
| 1.0865 | 10.0 | 3750 | 1.2459 | 0.3517 |
| 1.1578 | 11.0 | 4125 | 1.2399 | 0.3533 |
| 1.1516 | 12.0 | 4500 | 1.2341 | 0.355 |
| 1.1216 | 13.0 | 4875 | 1.2282 | 0.355 |
| 1.1365 | 14.0 | 5250 | 1.2228 | 0.3583 |
| 1.1282 | 15.0 | 5625 | 1.2175 | 0.3583 |
| 1.1187 | 16.0 | 6000 | 1.2123 | 0.3633 |
| 1.1048 | 17.0 | 6375 | 1.2074 | 0.365 |
| 1.1548 | 18.0 | 6750 | 1.2025 | 0.365 |
| 1.1271 | 19.0 | 7125 | 1.1978 | 0.3683 |
| 1.1003 | 20.0 | 7500 | 1.1934 | 0.3717 |
| 1.0771 | 21.0 | 7875 | 1.1891 | 0.3733 |
| 1.0833 | 22.0 | 8250 | 1.1849 | 0.3767 |
| 1.1002 | 23.0 | 8625 | 1.1809 | 0.3783 |
| 1.0994 | 24.0 | 9000 | 1.1772 | 0.3833 |
| 1.0715 | 25.0 | 9375 | 1.1735 | 0.385 |
| 1.1029 | 26.0 | 9750 | 1.1700 | 0.3867 |
| 1.1056 | 27.0 | 10125 | 1.1666 | 0.3867 |
| 1.022 | 28.0 | 10500 | 1.1633 | 0.3883 |
| 1.0343 | 29.0 | 10875 | 1.1602 | 0.3867 |
| 1.0325 | 30.0 | 11250 | 1.1573 | 0.3883 |
| 1.0378 | 31.0 | 11625 | 1.1546 | 0.3883 |
| 1.0659 | 32.0 | 12000 | 1.1519 | 0.3867 |
| 1.0282 | 33.0 | 12375 | 1.1495 | 0.3867 |
| 1.0519 | 34.0 | 12750 | 1.1472 | 0.3883 |
| 1.0399 | 35.0 | 13125 | 1.1451 | 0.3883 |
| 1.0632 | 36.0 | 13500 | 1.1430 | 0.39 |
| 1.015 | 37.0 | 13875 | 1.1411 | 0.39 |
| 1.0714 | 38.0 | 14250 | 1.1394 | 0.39 |
| 0.9921 | 39.0 | 14625 | 1.1379 | 0.3917 |
| 1.0391 | 40.0 | 15000 | 1.1365 | 0.3917 |
| 1.0121 | 41.0 | 15375 | 1.1352 | 0.395 |
| 1.0675 | 42.0 | 15750 | 1.1341 | 0.3967 |
| 1.0815 | 43.0 | 16125 | 1.1331 | 0.3967 |
| 1.0054 | 44.0 | 16500 | 1.1322 | 0.3967 |
| 1.0674 | 45.0 | 16875 | 1.1316 | 0.3983 |
| 1.0115 | 46.0 | 17250 | 1.1310 | 0.4 |
| 1.0426 | 47.0 | 17625 | 1.1306 | 0.4017 |
| 1.0416 | 48.0 | 18000 | 1.1303 | 0.4017 |
| 1.0297 | 49.0 | 18375 | 1.1302 | 0.4017 |
| 1.0431 | 50.0 | 18750 | 1.1301 | 0.4017 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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