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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: hushem_5x_beit_base_rms_00001_fold2
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: 0.8666666666666667
---
<!-- 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. -->
# hushem_5x_beit_base_rms_00001_fold2
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: 0.8184
- Accuracy: 0.8667
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6865 | 1.0 | 27 | 0.7969 | 0.7556 |
| 0.1615 | 2.0 | 54 | 0.9353 | 0.7778 |
| 0.041 | 3.0 | 81 | 1.0745 | 0.6444 |
| 0.0119 | 4.0 | 108 | 1.0481 | 0.7333 |
| 0.0095 | 5.0 | 135 | 0.6063 | 0.8667 |
| 0.0013 | 6.0 | 162 | 0.6520 | 0.8444 |
| 0.0015 | 7.0 | 189 | 0.7604 | 0.8667 |
| 0.0013 | 8.0 | 216 | 0.7595 | 0.8444 |
| 0.0008 | 9.0 | 243 | 0.8299 | 0.8444 |
| 0.0008 | 10.0 | 270 | 0.6509 | 0.8444 |
| 0.0009 | 11.0 | 297 | 0.7989 | 0.8444 |
| 0.0002 | 12.0 | 324 | 0.8458 | 0.8444 |
| 0.0005 | 13.0 | 351 | 0.6321 | 0.8667 |
| 0.0002 | 14.0 | 378 | 0.6972 | 0.8444 |
| 0.0002 | 15.0 | 405 | 0.7426 | 0.8667 |
| 0.0005 | 16.0 | 432 | 0.9776 | 0.8 |
| 0.0023 | 17.0 | 459 | 1.0180 | 0.8 |
| 0.0003 | 18.0 | 486 | 1.1105 | 0.7778 |
| 0.0006 | 19.0 | 513 | 0.9919 | 0.7556 |
| 0.0002 | 20.0 | 540 | 1.0177 | 0.8 |
| 0.0012 | 21.0 | 567 | 0.9992 | 0.8444 |
| 0.0003 | 22.0 | 594 | 0.9760 | 0.8444 |
| 0.0047 | 23.0 | 621 | 0.9891 | 0.8 |
| 0.0061 | 24.0 | 648 | 0.9730 | 0.8222 |
| 0.0002 | 25.0 | 675 | 0.8247 | 0.8222 |
| 0.0001 | 26.0 | 702 | 0.8270 | 0.8667 |
| 0.0001 | 27.0 | 729 | 0.7978 | 0.8222 |
| 0.0 | 28.0 | 756 | 0.8136 | 0.8444 |
| 0.0001 | 29.0 | 783 | 0.8553 | 0.8444 |
| 0.0001 | 30.0 | 810 | 0.9423 | 0.8444 |
| 0.0001 | 31.0 | 837 | 0.9286 | 0.8222 |
| 0.0001 | 32.0 | 864 | 0.9464 | 0.8222 |
| 0.0002 | 33.0 | 891 | 0.8713 | 0.8444 |
| 0.0001 | 34.0 | 918 | 0.8762 | 0.8444 |
| 0.0001 | 35.0 | 945 | 0.9092 | 0.8667 |
| 0.0 | 36.0 | 972 | 0.9547 | 0.8444 |
| 0.0 | 37.0 | 999 | 0.9283 | 0.8444 |
| 0.0 | 38.0 | 1026 | 0.8639 | 0.8444 |
| 0.0001 | 39.0 | 1053 | 0.8477 | 0.8667 |
| 0.0 | 40.0 | 1080 | 0.8432 | 0.8667 |
| 0.0 | 41.0 | 1107 | 0.8325 | 0.8667 |
| 0.0 | 42.0 | 1134 | 0.7851 | 0.8667 |
| 0.0003 | 43.0 | 1161 | 0.7875 | 0.8667 |
| 0.0 | 44.0 | 1188 | 0.7888 | 0.8667 |
| 0.0001 | 45.0 | 1215 | 0.8006 | 0.8889 |
| 0.0001 | 46.0 | 1242 | 0.8075 | 0.8889 |
| 0.0001 | 47.0 | 1269 | 0.8158 | 0.8889 |
| 0.0 | 48.0 | 1296 | 0.8184 | 0.8667 |
| 0.0002 | 49.0 | 1323 | 0.8184 | 0.8667 |
| 0.0001 | 50.0 | 1350 | 0.8184 | 0.8667 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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