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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_3x_beit_base_sgd_0001_fold4
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: 0.77
---
<!-- 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_3x_beit_base_sgd_0001_fold4
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: 0.5543
- Accuracy: 0.77
## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2016 | 1.0 | 225 | 1.2841 | 0.345 |
| 1.1719 | 2.0 | 450 | 1.2211 | 0.3617 |
| 1.0758 | 3.0 | 675 | 1.1630 | 0.3733 |
| 1.0147 | 4.0 | 900 | 1.1086 | 0.4033 |
| 1.0074 | 5.0 | 1125 | 1.0560 | 0.4317 |
| 0.9405 | 6.0 | 1350 | 1.0063 | 0.4617 |
| 0.9199 | 7.0 | 1575 | 0.9602 | 0.51 |
| 0.9125 | 8.0 | 1800 | 0.9177 | 0.5617 |
| 0.8654 | 9.0 | 2025 | 0.8771 | 0.6017 |
| 0.8229 | 10.0 | 2250 | 0.8432 | 0.6333 |
| 0.8209 | 11.0 | 2475 | 0.8129 | 0.6567 |
| 0.775 | 12.0 | 2700 | 0.7860 | 0.675 |
| 0.7435 | 13.0 | 2925 | 0.7620 | 0.6883 |
| 0.7034 | 14.0 | 3150 | 0.7408 | 0.695 |
| 0.7434 | 15.0 | 3375 | 0.7223 | 0.7033 |
| 0.7412 | 16.0 | 3600 | 0.7055 | 0.7133 |
| 0.6871 | 17.0 | 3825 | 0.6906 | 0.7167 |
| 0.6997 | 18.0 | 4050 | 0.6769 | 0.725 |
| 0.6998 | 19.0 | 4275 | 0.6646 | 0.7267 |
| 0.6623 | 20.0 | 4500 | 0.6540 | 0.7283 |
| 0.668 | 21.0 | 4725 | 0.6441 | 0.73 |
| 0.6697 | 22.0 | 4950 | 0.6349 | 0.7317 |
| 0.6394 | 23.0 | 5175 | 0.6268 | 0.7383 |
| 0.6267 | 24.0 | 5400 | 0.6193 | 0.7383 |
| 0.6154 | 25.0 | 5625 | 0.6125 | 0.7433 |
| 0.5813 | 26.0 | 5850 | 0.6070 | 0.745 |
| 0.612 | 27.0 | 6075 | 0.6014 | 0.7483 |
| 0.6011 | 28.0 | 6300 | 0.5964 | 0.7483 |
| 0.5913 | 29.0 | 6525 | 0.5915 | 0.7517 |
| 0.5609 | 30.0 | 6750 | 0.5872 | 0.76 |
| 0.5861 | 31.0 | 6975 | 0.5835 | 0.7617 |
| 0.5483 | 32.0 | 7200 | 0.5800 | 0.76 |
| 0.5986 | 33.0 | 7425 | 0.5766 | 0.7633 |
| 0.619 | 34.0 | 7650 | 0.5736 | 0.7617 |
| 0.5813 | 35.0 | 7875 | 0.5710 | 0.765 |
| 0.6084 | 36.0 | 8100 | 0.5683 | 0.7667 |
| 0.6052 | 37.0 | 8325 | 0.5664 | 0.765 |
| 0.5601 | 38.0 | 8550 | 0.5646 | 0.765 |
| 0.5878 | 39.0 | 8775 | 0.5631 | 0.7633 |
| 0.6072 | 40.0 | 9000 | 0.5616 | 0.7633 |
| 0.5597 | 41.0 | 9225 | 0.5601 | 0.7683 |
| 0.5694 | 42.0 | 9450 | 0.5588 | 0.7667 |
| 0.5553 | 43.0 | 9675 | 0.5575 | 0.77 |
| 0.5942 | 44.0 | 9900 | 0.5566 | 0.77 |
| 0.6005 | 45.0 | 10125 | 0.5559 | 0.77 |
| 0.58 | 46.0 | 10350 | 0.5553 | 0.77 |
| 0.5814 | 47.0 | 10575 | 0.5548 | 0.77 |
| 0.5609 | 48.0 | 10800 | 0.5545 | 0.7717 |
| 0.6076 | 49.0 | 11025 | 0.5543 | 0.77 |
| 0.5819 | 50.0 | 11250 | 0.5543 | 0.77 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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