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---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_base_rms_0001_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: 0.8933333333333333
---
<!-- 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_base_rms_0001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1319
- Accuracy: 0.8933
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3269 | 1.0 | 225 | 0.4237 | 0.8533 |
| 0.1863 | 2.0 | 450 | 0.4104 | 0.8633 |
| 0.1323 | 3.0 | 675 | 0.3455 | 0.8767 |
| 0.0674 | 4.0 | 900 | 0.4806 | 0.895 |
| 0.0524 | 5.0 | 1125 | 0.4637 | 0.8783 |
| 0.0407 | 6.0 | 1350 | 0.4567 | 0.89 |
| 0.0468 | 7.0 | 1575 | 0.5568 | 0.8767 |
| 0.0239 | 8.0 | 1800 | 0.6027 | 0.8783 |
| 0.0176 | 9.0 | 2025 | 0.6627 | 0.8817 |
| 0.0132 | 10.0 | 2250 | 0.7320 | 0.8683 |
| 0.0166 | 11.0 | 2475 | 0.6923 | 0.88 |
| 0.11 | 12.0 | 2700 | 0.5801 | 0.8883 |
| 0.0376 | 13.0 | 2925 | 0.4794 | 0.89 |
| 0.0285 | 14.0 | 3150 | 0.6473 | 0.8883 |
| 0.0192 | 15.0 | 3375 | 0.7068 | 0.8967 |
| 0.0041 | 16.0 | 3600 | 0.7011 | 0.895 |
| 0.012 | 17.0 | 3825 | 0.6525 | 0.9017 |
| 0.03 | 18.0 | 4050 | 0.6508 | 0.91 |
| 0.0251 | 19.0 | 4275 | 0.7493 | 0.8967 |
| 0.0108 | 20.0 | 4500 | 0.7077 | 0.895 |
| 0.0009 | 21.0 | 4725 | 0.6790 | 0.89 |
| 0.0002 | 22.0 | 4950 | 0.7411 | 0.8967 |
| 0.0264 | 23.0 | 5175 | 0.7794 | 0.8983 |
| 0.0051 | 24.0 | 5400 | 0.9553 | 0.8883 |
| 0.0221 | 25.0 | 5625 | 0.7771 | 0.905 |
| 0.0315 | 26.0 | 5850 | 0.7638 | 0.9 |
| 0.003 | 27.0 | 6075 | 0.8047 | 0.9 |
| 0.0125 | 28.0 | 6300 | 0.7560 | 0.9 |
| 0.0039 | 29.0 | 6525 | 0.7149 | 0.9067 |
| 0.0 | 30.0 | 6750 | 0.8257 | 0.9 |
| 0.0 | 31.0 | 6975 | 0.8249 | 0.9133 |
| 0.0 | 32.0 | 7200 | 0.8553 | 0.9033 |
| 0.01 | 33.0 | 7425 | 0.9333 | 0.895 |
| 0.0 | 34.0 | 7650 | 0.9286 | 0.9067 |
| 0.0024 | 35.0 | 7875 | 0.9413 | 0.8983 |
| 0.0 | 36.0 | 8100 | 0.8868 | 0.9083 |
| 0.0039 | 37.0 | 8325 | 0.9484 | 0.9033 |
| 0.0 | 38.0 | 8550 | 0.9617 | 0.9033 |
| 0.0 | 39.0 | 8775 | 0.9572 | 0.9017 |
| 0.0 | 40.0 | 9000 | 1.0465 | 0.8933 |
| 0.0 | 41.0 | 9225 | 1.0197 | 0.8983 |
| 0.0 | 42.0 | 9450 | 1.0477 | 0.895 |
| 0.0029 | 43.0 | 9675 | 1.0659 | 0.8983 |
| 0.0 | 44.0 | 9900 | 1.0846 | 0.8967 |
| 0.0 | 45.0 | 10125 | 1.1008 | 0.8983 |
| 0.0 | 46.0 | 10350 | 1.1123 | 0.8917 |
| 0.0 | 47.0 | 10575 | 1.1192 | 0.8933 |
| 0.0 | 48.0 | 10800 | 1.1251 | 0.8933 |
| 0.0 | 49.0 | 11025 | 1.1289 | 0.8933 |
| 0.0 | 50.0 | 11250 | 1.1319 | 0.8933 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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