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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_rms_001_fold3
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: 0.7883333333333333
---
<!-- 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_rms_001_fold3
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: 1.2450
- Accuracy: 0.7883
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8383 | 1.0 | 375 | 0.9251 | 0.4967 |
| 0.7811 | 2.0 | 750 | 0.8274 | 0.55 |
| 0.7757 | 3.0 | 1125 | 0.8322 | 0.55 |
| 0.774 | 4.0 | 1500 | 0.7903 | 0.5667 |
| 0.7988 | 5.0 | 1875 | 0.7818 | 0.59 |
| 0.7926 | 6.0 | 2250 | 0.7711 | 0.595 |
| 0.7549 | 7.0 | 2625 | 0.7682 | 0.6267 |
| 0.7997 | 8.0 | 3000 | 0.7569 | 0.61 |
| 0.6926 | 9.0 | 3375 | 0.7561 | 0.6417 |
| 0.7413 | 10.0 | 3750 | 0.7251 | 0.6567 |
| 0.6722 | 11.0 | 4125 | 0.7285 | 0.6533 |
| 0.7582 | 12.0 | 4500 | 0.7029 | 0.66 |
| 0.6728 | 13.0 | 4875 | 0.7283 | 0.6433 |
| 0.6373 | 14.0 | 5250 | 0.7252 | 0.6333 |
| 0.648 | 15.0 | 5625 | 0.7000 | 0.67 |
| 0.6675 | 16.0 | 6000 | 0.7072 | 0.6683 |
| 0.7316 | 17.0 | 6375 | 0.7063 | 0.6717 |
| 0.7151 | 18.0 | 6750 | 0.6856 | 0.6683 |
| 0.6082 | 19.0 | 7125 | 0.6800 | 0.6817 |
| 0.6879 | 20.0 | 7500 | 0.6816 | 0.6733 |
| 0.5586 | 21.0 | 7875 | 0.6735 | 0.695 |
| 0.6065 | 22.0 | 8250 | 0.6507 | 0.71 |
| 0.5783 | 23.0 | 8625 | 0.6597 | 0.69 |
| 0.6456 | 24.0 | 9000 | 0.6102 | 0.74 |
| 0.5238 | 25.0 | 9375 | 0.6683 | 0.7117 |
| 0.5326 | 26.0 | 9750 | 0.6240 | 0.7183 |
| 0.5499 | 27.0 | 10125 | 0.6403 | 0.7083 |
| 0.5607 | 28.0 | 10500 | 0.5945 | 0.7417 |
| 0.4887 | 29.0 | 10875 | 0.6536 | 0.71 |
| 0.5354 | 30.0 | 11250 | 0.5785 | 0.725 |
| 0.5136 | 31.0 | 11625 | 0.6072 | 0.7517 |
| 0.5448 | 32.0 | 12000 | 0.6265 | 0.7383 |
| 0.4542 | 33.0 | 12375 | 0.6265 | 0.7417 |
| 0.4208 | 34.0 | 12750 | 0.6113 | 0.745 |
| 0.3509 | 35.0 | 13125 | 0.6279 | 0.7467 |
| 0.4112 | 36.0 | 13500 | 0.6145 | 0.74 |
| 0.3719 | 37.0 | 13875 | 0.6674 | 0.745 |
| 0.3029 | 38.0 | 14250 | 0.6977 | 0.7583 |
| 0.3416 | 39.0 | 14625 | 0.6751 | 0.7717 |
| 0.3246 | 40.0 | 15000 | 0.6878 | 0.7633 |
| 0.2432 | 41.0 | 15375 | 0.6417 | 0.79 |
| 0.2014 | 42.0 | 15750 | 0.7882 | 0.78 |
| 0.2354 | 43.0 | 16125 | 0.8175 | 0.7817 |
| 0.1797 | 44.0 | 16500 | 0.8553 | 0.79 |
| 0.1419 | 45.0 | 16875 | 0.9481 | 0.765 |
| 0.1815 | 46.0 | 17250 | 1.0306 | 0.765 |
| 0.1604 | 47.0 | 17625 | 1.0263 | 0.765 |
| 0.103 | 48.0 | 18000 | 1.1281 | 0.7833 |
| 0.0441 | 49.0 | 18375 | 1.2055 | 0.79 |
| 0.0741 | 50.0 | 18750 | 1.2450 | 0.7883 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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