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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_10x_deit_small_rms_0001_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: 0.905
---
<!-- 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_10x_deit_small_rms_0001_fold3
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: 1.0751
- Accuracy: 0.905
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2232 | 1.0 | 750 | 0.2644 | 0.9033 |
| 0.1749 | 2.0 | 1500 | 0.3888 | 0.87 |
| 0.0708 | 3.0 | 2250 | 0.3860 | 0.9083 |
| 0.0864 | 4.0 | 3000 | 0.4018 | 0.9017 |
| 0.027 | 5.0 | 3750 | 0.5540 | 0.905 |
| 0.0152 | 6.0 | 4500 | 0.5713 | 0.9133 |
| 0.0637 | 7.0 | 5250 | 0.5356 | 0.9017 |
| 0.0914 | 8.0 | 6000 | 0.5134 | 0.9117 |
| 0.0042 | 9.0 | 6750 | 0.5226 | 0.905 |
| 0.0385 | 10.0 | 7500 | 0.6176 | 0.9067 |
| 0.0198 | 11.0 | 8250 | 0.7633 | 0.89 |
| 0.0448 | 12.0 | 9000 | 0.7337 | 0.8783 |
| 0.0021 | 13.0 | 9750 | 0.6859 | 0.8917 |
| 0.0019 | 14.0 | 10500 | 0.7372 | 0.8933 |
| 0.0006 | 15.0 | 11250 | 0.7460 | 0.885 |
| 0.0195 | 16.0 | 12000 | 0.7805 | 0.8933 |
| 0.0202 | 17.0 | 12750 | 0.8243 | 0.895 |
| 0.016 | 18.0 | 13500 | 0.7845 | 0.89 |
| 0.0037 | 19.0 | 14250 | 0.7538 | 0.8883 |
| 0.0001 | 20.0 | 15000 | 0.6925 | 0.8967 |
| 0.0006 | 21.0 | 15750 | 0.8393 | 0.8933 |
| 0.0 | 22.0 | 16500 | 0.7236 | 0.9 |
| 0.0024 | 23.0 | 17250 | 0.8639 | 0.885 |
| 0.0014 | 24.0 | 18000 | 0.8799 | 0.8917 |
| 0.0236 | 25.0 | 18750 | 0.6893 | 0.9033 |
| 0.0001 | 26.0 | 19500 | 0.7435 | 0.9033 |
| 0.0001 | 27.0 | 20250 | 0.6829 | 0.89 |
| 0.0194 | 28.0 | 21000 | 0.8267 | 0.8967 |
| 0.0002 | 29.0 | 21750 | 0.8000 | 0.8983 |
| 0.0001 | 30.0 | 22500 | 0.8336 | 0.89 |
| 0.0 | 31.0 | 23250 | 0.8017 | 0.9 |
| 0.0 | 32.0 | 24000 | 0.8257 | 0.9117 |
| 0.0 | 33.0 | 24750 | 0.8456 | 0.905 |
| 0.0 | 34.0 | 25500 | 0.7637 | 0.91 |
| 0.0 | 35.0 | 26250 | 0.8426 | 0.9067 |
| 0.0219 | 36.0 | 27000 | 0.8594 | 0.9067 |
| 0.0 | 37.0 | 27750 | 0.8437 | 0.9083 |
| 0.0 | 38.0 | 28500 | 0.9026 | 0.9117 |
| 0.0 | 39.0 | 29250 | 0.9566 | 0.9067 |
| 0.0 | 40.0 | 30000 | 0.9200 | 0.915 |
| 0.0 | 41.0 | 30750 | 0.9067 | 0.92 |
| 0.0 | 42.0 | 31500 | 0.9289 | 0.91 |
| 0.0 | 43.0 | 32250 | 0.9815 | 0.91 |
| 0.0 | 44.0 | 33000 | 0.9712 | 0.91 |
| 0.0 | 45.0 | 33750 | 1.0254 | 0.9067 |
| 0.0 | 46.0 | 34500 | 1.0353 | 0.9083 |
| 0.0 | 47.0 | 35250 | 1.0450 | 0.905 |
| 0.0 | 48.0 | 36000 | 1.0661 | 0.905 |
| 0.0 | 49.0 | 36750 | 1.0715 | 0.905 |
| 0.0 | 50.0 | 37500 | 1.0751 | 0.905 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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