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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_1x_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: 0.8444444444444444
---
<!-- 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_1x_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: 0.9358
- Accuracy: 0.8444
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.2856 | 0.4889 |
| 1.4398 | 2.0 | 12 | 0.9696 | 0.6222 |
| 1.4398 | 3.0 | 18 | 0.7405 | 0.7111 |
| 0.463 | 4.0 | 24 | 0.8561 | 0.7333 |
| 0.1243 | 5.0 | 30 | 0.6572 | 0.8222 |
| 0.1243 | 6.0 | 36 | 0.6983 | 0.8444 |
| 0.0205 | 7.0 | 42 | 0.7294 | 0.8222 |
| 0.0205 | 8.0 | 48 | 0.6504 | 0.8 |
| 0.0064 | 9.0 | 54 | 0.6828 | 0.8222 |
| 0.0142 | 10.0 | 60 | 0.6539 | 0.8222 |
| 0.0142 | 11.0 | 66 | 0.7615 | 0.8444 |
| 0.0032 | 12.0 | 72 | 0.8146 | 0.8444 |
| 0.0032 | 13.0 | 78 | 0.8154 | 0.8444 |
| 0.0019 | 14.0 | 84 | 0.7947 | 0.8444 |
| 0.0028 | 15.0 | 90 | 0.7939 | 0.8444 |
| 0.0028 | 16.0 | 96 | 0.8240 | 0.8444 |
| 0.0013 | 17.0 | 102 | 0.8242 | 0.8222 |
| 0.0013 | 18.0 | 108 | 0.8443 | 0.8444 |
| 0.0014 | 19.0 | 114 | 0.8393 | 0.8444 |
| 0.0012 | 20.0 | 120 | 0.9165 | 0.8222 |
| 0.0012 | 21.0 | 126 | 0.8985 | 0.8222 |
| 0.0008 | 22.0 | 132 | 0.9053 | 0.8222 |
| 0.0008 | 23.0 | 138 | 0.9182 | 0.8222 |
| 0.0007 | 24.0 | 144 | 0.9131 | 0.8222 |
| 0.0007 | 25.0 | 150 | 0.9205 | 0.8222 |
| 0.0007 | 26.0 | 156 | 0.9165 | 0.8222 |
| 0.0004 | 27.0 | 162 | 0.9119 | 0.8222 |
| 0.0004 | 28.0 | 168 | 0.9185 | 0.8222 |
| 0.0005 | 29.0 | 174 | 0.9203 | 0.8222 |
| 0.0004 | 30.0 | 180 | 0.9232 | 0.8222 |
| 0.0004 | 31.0 | 186 | 0.9207 | 0.8444 |
| 0.0009 | 32.0 | 192 | 0.9256 | 0.8444 |
| 0.0009 | 33.0 | 198 | 0.9230 | 0.8444 |
| 0.0082 | 34.0 | 204 | 0.9200 | 0.8444 |
| 0.0007 | 35.0 | 210 | 0.9385 | 0.8444 |
| 0.0007 | 36.0 | 216 | 0.9350 | 0.8444 |
| 0.0005 | 37.0 | 222 | 0.9367 | 0.8444 |
| 0.0005 | 38.0 | 228 | 0.9290 | 0.8444 |
| 0.0044 | 39.0 | 234 | 0.9294 | 0.8444 |
| 0.0005 | 40.0 | 240 | 0.9330 | 0.8444 |
| 0.0005 | 41.0 | 246 | 0.9359 | 0.8444 |
| 0.0006 | 42.0 | 252 | 0.9358 | 0.8444 |
| 0.0006 | 43.0 | 258 | 0.9358 | 0.8444 |
| 0.0005 | 44.0 | 264 | 0.9358 | 0.8444 |
| 0.0007 | 45.0 | 270 | 0.9358 | 0.8444 |
| 0.0007 | 46.0 | 276 | 0.9358 | 0.8444 |
| 0.0007 | 47.0 | 282 | 0.9358 | 0.8444 |
| 0.0007 | 48.0 | 288 | 0.9358 | 0.8444 |
| 0.0006 | 49.0 | 294 | 0.9358 | 0.8444 |
| 0.0004 | 50.0 | 300 | 0.9358 | 0.8444 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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