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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_1x_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: 0.8683333333333333
---
<!-- 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_1x_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: 1.1062
- Accuracy: 0.8683
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9975 | 1.0 | 75 | 0.8496 | 0.5467 |
| 0.5061 | 2.0 | 150 | 0.5223 | 0.8067 |
| 0.3795 | 3.0 | 225 | 0.4405 | 0.8267 |
| 0.3892 | 4.0 | 300 | 0.3713 | 0.8533 |
| 0.1768 | 5.0 | 375 | 0.3582 | 0.88 |
| 0.1554 | 6.0 | 450 | 0.4330 | 0.8733 |
| 0.1428 | 7.0 | 525 | 0.4207 | 0.8567 |
| 0.0674 | 8.0 | 600 | 0.6040 | 0.8567 |
| 0.0307 | 9.0 | 675 | 0.7767 | 0.8317 |
| 0.0367 | 10.0 | 750 | 0.6480 | 0.8567 |
| 0.0581 | 11.0 | 825 | 0.6494 | 0.87 |
| 0.0608 | 12.0 | 900 | 0.5071 | 0.8667 |
| 0.0462 | 13.0 | 975 | 0.7332 | 0.855 |
| 0.0198 | 14.0 | 1050 | 0.7960 | 0.8633 |
| 0.0291 | 15.0 | 1125 | 0.7675 | 0.8683 |
| 0.0004 | 16.0 | 1200 | 0.8666 | 0.8567 |
| 0.0394 | 17.0 | 1275 | 0.8320 | 0.8667 |
| 0.0137 | 18.0 | 1350 | 0.8206 | 0.86 |
| 0.0055 | 19.0 | 1425 | 0.9665 | 0.8583 |
| 0.029 | 20.0 | 1500 | 0.8497 | 0.8683 |
| 0.0429 | 21.0 | 1575 | 0.9318 | 0.8717 |
| 0.0315 | 22.0 | 1650 | 0.9188 | 0.8567 |
| 0.0182 | 23.0 | 1725 | 0.8073 | 0.875 |
| 0.0239 | 24.0 | 1800 | 0.9607 | 0.8683 |
| 0.0057 | 25.0 | 1875 | 0.8991 | 0.8767 |
| 0.004 | 26.0 | 1950 | 0.8719 | 0.8633 |
| 0.0226 | 27.0 | 2025 | 0.8720 | 0.8533 |
| 0.0534 | 28.0 | 2100 | 0.8637 | 0.8633 |
| 0.0299 | 29.0 | 2175 | 0.9839 | 0.865 |
| 0.0001 | 30.0 | 2250 | 0.9564 | 0.8667 |
| 0.0001 | 31.0 | 2325 | 0.9281 | 0.8783 |
| 0.0024 | 32.0 | 2400 | 0.9454 | 0.875 |
| 0.0023 | 33.0 | 2475 | 0.9716 | 0.875 |
| 0.0055 | 34.0 | 2550 | 0.9822 | 0.875 |
| 0.009 | 35.0 | 2625 | 0.9930 | 0.865 |
| 0.0029 | 36.0 | 2700 | 1.0435 | 0.8717 |
| 0.0019 | 37.0 | 2775 | 1.0502 | 0.8683 |
| 0.0 | 38.0 | 2850 | 1.0112 | 0.87 |
| 0.0 | 39.0 | 2925 | 1.0171 | 0.8733 |
| 0.0 | 40.0 | 3000 | 1.0381 | 0.8733 |
| 0.0 | 41.0 | 3075 | 1.0120 | 0.8667 |
| 0.0026 | 42.0 | 3150 | 1.0208 | 0.8667 |
| 0.0025 | 43.0 | 3225 | 1.0419 | 0.8683 |
| 0.0 | 44.0 | 3300 | 1.0612 | 0.87 |
| 0.0025 | 45.0 | 3375 | 1.0735 | 0.8633 |
| 0.0025 | 46.0 | 3450 | 1.0868 | 0.8667 |
| 0.0049 | 47.0 | 3525 | 1.0931 | 0.87 |
| 0.0 | 48.0 | 3600 | 1.0992 | 0.8683 |
| 0.0 | 49.0 | 3675 | 1.1039 | 0.87 |
| 0.0045 | 50.0 | 3750 | 1.1062 | 0.8683 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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