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---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_small_rms_0001_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: 0.8801996672212978
---
<!-- 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_deit_small_rms_0001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1115
- Accuracy: 0.8802
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3676 | 1.0 | 225 | 0.3260 | 0.8652 |
| 0.2682 | 2.0 | 450 | 0.4038 | 0.8369 |
| 0.1969 | 3.0 | 675 | 0.3463 | 0.8569 |
| 0.1563 | 4.0 | 900 | 0.3656 | 0.8869 |
| 0.127 | 5.0 | 1125 | 0.4906 | 0.8885 |
| 0.0496 | 6.0 | 1350 | 0.4366 | 0.8852 |
| 0.0874 | 7.0 | 1575 | 0.6811 | 0.8735 |
| 0.0746 | 8.0 | 1800 | 0.4728 | 0.9002 |
| 0.0301 | 9.0 | 2025 | 0.6425 | 0.8802 |
| 0.0064 | 10.0 | 2250 | 0.6457 | 0.8852 |
| 0.021 | 11.0 | 2475 | 0.6671 | 0.8752 |
| 0.0135 | 12.0 | 2700 | 0.6914 | 0.8852 |
| 0.0087 | 13.0 | 2925 | 0.8348 | 0.8686 |
| 0.0257 | 14.0 | 3150 | 0.6378 | 0.8769 |
| 0.0699 | 15.0 | 3375 | 0.7199 | 0.8885 |
| 0.003 | 16.0 | 3600 | 0.7607 | 0.8869 |
| 0.003 | 17.0 | 3825 | 0.7580 | 0.8819 |
| 0.0003 | 18.0 | 4050 | 0.7463 | 0.8835 |
| 0.0005 | 19.0 | 4275 | 0.6721 | 0.8852 |
| 0.0305 | 20.0 | 4500 | 0.7465 | 0.8785 |
| 0.03 | 21.0 | 4725 | 0.8137 | 0.8752 |
| 0.0098 | 22.0 | 4950 | 0.7797 | 0.8802 |
| 0.0223 | 23.0 | 5175 | 0.8830 | 0.8735 |
| 0.0014 | 24.0 | 5400 | 0.9177 | 0.8752 |
| 0.0318 | 25.0 | 5625 | 1.2159 | 0.8519 |
| 0.0263 | 26.0 | 5850 | 0.9640 | 0.8669 |
| 0.0494 | 27.0 | 6075 | 0.9004 | 0.8702 |
| 0.0002 | 28.0 | 6300 | 1.0163 | 0.8752 |
| 0.0354 | 29.0 | 6525 | 1.0067 | 0.8752 |
| 0.0062 | 30.0 | 6750 | 1.0029 | 0.8785 |
| 0.0239 | 31.0 | 6975 | 0.8464 | 0.8835 |
| 0.0305 | 32.0 | 7200 | 0.8764 | 0.8752 |
| 0.0007 | 33.0 | 7425 | 0.8617 | 0.8769 |
| 0.0 | 34.0 | 7650 | 0.9176 | 0.8785 |
| 0.0 | 35.0 | 7875 | 0.9537 | 0.8885 |
| 0.0028 | 36.0 | 8100 | 0.9078 | 0.8802 |
| 0.0 | 37.0 | 8325 | 0.9401 | 0.8902 |
| 0.0066 | 38.0 | 8550 | 0.9351 | 0.8802 |
| 0.0208 | 39.0 | 8775 | 0.9403 | 0.8869 |
| 0.0 | 40.0 | 9000 | 1.0137 | 0.8852 |
| 0.0103 | 41.0 | 9225 | 1.0628 | 0.8769 |
| 0.0 | 42.0 | 9450 | 0.9758 | 0.8802 |
| 0.0 | 43.0 | 9675 | 1.0037 | 0.8802 |
| 0.0 | 44.0 | 9900 | 1.0404 | 0.8769 |
| 0.0 | 45.0 | 10125 | 1.0618 | 0.8819 |
| 0.0 | 46.0 | 10350 | 1.0847 | 0.8802 |
| 0.0 | 47.0 | 10575 | 1.0984 | 0.8819 |
| 0.0 | 48.0 | 10800 | 1.1045 | 0.8819 |
| 0.0023 | 49.0 | 11025 | 1.1110 | 0.8802 |
| 0.0023 | 50.0 | 11250 | 1.1115 | 0.8802 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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