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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_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: 0.7783333333333333
---
<!-- 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_sgd_0001_fold4
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: 0.5687
- Accuracy: 0.7783
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0247 | 1.0 | 225 | 1.0422 | 0.45 |
| 1.0237 | 2.0 | 450 | 1.0174 | 0.4883 |
| 0.9838 | 3.0 | 675 | 0.9928 | 0.52 |
| 0.9769 | 4.0 | 900 | 0.9683 | 0.555 |
| 0.9529 | 5.0 | 1125 | 0.9446 | 0.5867 |
| 0.9169 | 6.0 | 1350 | 0.9209 | 0.6017 |
| 0.9264 | 7.0 | 1575 | 0.8980 | 0.6083 |
| 0.9011 | 8.0 | 1800 | 0.8753 | 0.6267 |
| 0.8821 | 9.0 | 2025 | 0.8542 | 0.63 |
| 0.8381 | 10.0 | 2250 | 0.8337 | 0.66 |
| 0.8339 | 11.0 | 2475 | 0.8147 | 0.6683 |
| 0.8391 | 12.0 | 2700 | 0.7963 | 0.6833 |
| 0.8165 | 13.0 | 2925 | 0.7789 | 0.6933 |
| 0.736 | 14.0 | 3150 | 0.7623 | 0.7083 |
| 0.7819 | 15.0 | 3375 | 0.7468 | 0.725 |
| 0.7441 | 16.0 | 3600 | 0.7323 | 0.72 |
| 0.7169 | 17.0 | 3825 | 0.7189 | 0.7333 |
| 0.7451 | 18.0 | 4050 | 0.7062 | 0.7383 |
| 0.7048 | 19.0 | 4275 | 0.6943 | 0.74 |
| 0.6589 | 20.0 | 4500 | 0.6832 | 0.745 |
| 0.6884 | 21.0 | 4725 | 0.6730 | 0.7433 |
| 0.7041 | 22.0 | 4950 | 0.6635 | 0.745 |
| 0.6833 | 23.0 | 5175 | 0.6547 | 0.75 |
| 0.6669 | 24.0 | 5400 | 0.6465 | 0.7533 |
| 0.6608 | 25.0 | 5625 | 0.6391 | 0.7517 |
| 0.6311 | 26.0 | 5850 | 0.6322 | 0.7567 |
| 0.6676 | 27.0 | 6075 | 0.6258 | 0.7583 |
| 0.6172 | 28.0 | 6300 | 0.6199 | 0.7617 |
| 0.6339 | 29.0 | 6525 | 0.6146 | 0.765 |
| 0.6134 | 30.0 | 6750 | 0.6096 | 0.7717 |
| 0.6169 | 31.0 | 6975 | 0.6051 | 0.775 |
| 0.5976 | 32.0 | 7200 | 0.6008 | 0.7767 |
| 0.601 | 33.0 | 7425 | 0.5969 | 0.7767 |
| 0.6016 | 34.0 | 7650 | 0.5933 | 0.78 |
| 0.5916 | 35.0 | 7875 | 0.5900 | 0.78 |
| 0.6147 | 36.0 | 8100 | 0.5870 | 0.78 |
| 0.5896 | 37.0 | 8325 | 0.5843 | 0.78 |
| 0.5987 | 38.0 | 8550 | 0.5818 | 0.78 |
| 0.5562 | 39.0 | 8775 | 0.5795 | 0.78 |
| 0.6128 | 40.0 | 9000 | 0.5775 | 0.7817 |
| 0.5635 | 41.0 | 9225 | 0.5757 | 0.78 |
| 0.6047 | 42.0 | 9450 | 0.5742 | 0.78 |
| 0.5584 | 43.0 | 9675 | 0.5728 | 0.78 |
| 0.628 | 44.0 | 9900 | 0.5716 | 0.78 |
| 0.5798 | 45.0 | 10125 | 0.5707 | 0.7783 |
| 0.583 | 46.0 | 10350 | 0.5699 | 0.7783 |
| 0.5729 | 47.0 | 10575 | 0.5694 | 0.7783 |
| 0.5825 | 48.0 | 10800 | 0.5690 | 0.7783 |
| 0.6044 | 49.0 | 11025 | 0.5688 | 0.7783 |
| 0.5946 | 50.0 | 11250 | 0.5687 | 0.7783 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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