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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_001_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: 0.7616666666666667
---
<!-- 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_001_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: 0.6041
- Accuracy: 0.7617
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8603 | 1.0 | 375 | 0.8648 | 0.5183 |
| 0.8445 | 2.0 | 750 | 0.8098 | 0.5417 |
| 0.7944 | 3.0 | 1125 | 0.7826 | 0.5917 |
| 0.7602 | 4.0 | 1500 | 0.8095 | 0.6133 |
| 0.7358 | 5.0 | 1875 | 0.7702 | 0.62 |
| 0.7338 | 6.0 | 2250 | 0.7325 | 0.6383 |
| 0.7068 | 7.0 | 2625 | 0.7570 | 0.6267 |
| 0.7788 | 8.0 | 3000 | 0.7318 | 0.6183 |
| 0.7701 | 9.0 | 3375 | 0.7391 | 0.65 |
| 0.7025 | 10.0 | 3750 | 0.7251 | 0.6617 |
| 0.7076 | 11.0 | 4125 | 0.7171 | 0.6433 |
| 0.6226 | 12.0 | 4500 | 0.7139 | 0.6333 |
| 0.6825 | 13.0 | 4875 | 0.7299 | 0.63 |
| 0.6882 | 14.0 | 5250 | 0.7324 | 0.6517 |
| 0.7468 | 15.0 | 5625 | 0.6842 | 0.7 |
| 0.6568 | 16.0 | 6000 | 0.7213 | 0.6533 |
| 0.6593 | 17.0 | 6375 | 0.6880 | 0.6583 |
| 0.68 | 18.0 | 6750 | 0.6884 | 0.6733 |
| 0.6767 | 19.0 | 7125 | 0.7231 | 0.665 |
| 0.6609 | 20.0 | 7500 | 0.6577 | 0.6983 |
| 0.6233 | 21.0 | 7875 | 0.7352 | 0.6417 |
| 0.6128 | 22.0 | 8250 | 0.6662 | 0.695 |
| 0.6939 | 23.0 | 8625 | 0.7254 | 0.71 |
| 0.6892 | 24.0 | 9000 | 0.7067 | 0.695 |
| 0.5723 | 25.0 | 9375 | 0.6348 | 0.72 |
| 0.6474 | 26.0 | 9750 | 0.6506 | 0.7083 |
| 0.6695 | 27.0 | 10125 | 0.6672 | 0.6883 |
| 0.7033 | 28.0 | 10500 | 0.6914 | 0.6833 |
| 0.6792 | 29.0 | 10875 | 0.6764 | 0.685 |
| 0.5904 | 30.0 | 11250 | 0.6857 | 0.6883 |
| 0.5913 | 31.0 | 11625 | 0.6709 | 0.6933 |
| 0.5784 | 32.0 | 12000 | 0.7184 | 0.69 |
| 0.6212 | 33.0 | 12375 | 0.6393 | 0.7233 |
| 0.6674 | 34.0 | 12750 | 0.6697 | 0.71 |
| 0.5844 | 35.0 | 13125 | 0.6220 | 0.7283 |
| 0.5892 | 36.0 | 13500 | 0.6265 | 0.7217 |
| 0.572 | 37.0 | 13875 | 0.6315 | 0.7117 |
| 0.5345 | 38.0 | 14250 | 0.6267 | 0.7417 |
| 0.5582 | 39.0 | 14625 | 0.5952 | 0.7433 |
| 0.5947 | 40.0 | 15000 | 0.6182 | 0.715 |
| 0.5681 | 41.0 | 15375 | 0.6009 | 0.7533 |
| 0.5885 | 42.0 | 15750 | 0.6107 | 0.7367 |
| 0.5772 | 43.0 | 16125 | 0.5746 | 0.75 |
| 0.4378 | 44.0 | 16500 | 0.5833 | 0.755 |
| 0.5286 | 45.0 | 16875 | 0.6256 | 0.7417 |
| 0.538 | 46.0 | 17250 | 0.6036 | 0.7483 |
| 0.5732 | 47.0 | 17625 | 0.6044 | 0.76 |
| 0.4485 | 48.0 | 18000 | 0.5966 | 0.7533 |
| 0.4959 | 49.0 | 18375 | 0.6043 | 0.7583 |
| 0.4683 | 50.0 | 18750 | 0.6041 | 0.7617 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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