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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_adamax_00001_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: 0.8718801996672213
---
<!-- 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_adamax_00001_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: 1.1874
- Accuracy: 0.8719
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2546 | 1.0 | 750 | 0.2964 | 0.8885 |
| 0.1392 | 2.0 | 1500 | 0.2964 | 0.8935 |
| 0.1051 | 3.0 | 2250 | 0.3173 | 0.8802 |
| 0.0797 | 4.0 | 3000 | 0.3716 | 0.8802 |
| 0.0803 | 5.0 | 3750 | 0.4496 | 0.8769 |
| 0.0599 | 6.0 | 4500 | 0.5455 | 0.8769 |
| 0.0367 | 7.0 | 5250 | 0.6753 | 0.8686 |
| 0.0203 | 8.0 | 6000 | 0.7402 | 0.8752 |
| 0.0136 | 9.0 | 6750 | 0.8455 | 0.8686 |
| 0.0001 | 10.0 | 7500 | 0.8969 | 0.8686 |
| 0.0056 | 11.0 | 8250 | 0.9305 | 0.8769 |
| 0.0002 | 12.0 | 9000 | 0.9474 | 0.8752 |
| 0.0 | 13.0 | 9750 | 0.9957 | 0.8785 |
| 0.0 | 14.0 | 10500 | 1.0123 | 0.8769 |
| 0.0001 | 15.0 | 11250 | 0.9720 | 0.8835 |
| 0.0001 | 16.0 | 12000 | 1.0684 | 0.8785 |
| 0.0003 | 17.0 | 12750 | 1.1079 | 0.8752 |
| 0.0 | 18.0 | 13500 | 1.0971 | 0.8752 |
| 0.0 | 19.0 | 14250 | 1.0987 | 0.8735 |
| 0.0 | 20.0 | 15000 | 1.1190 | 0.8769 |
| 0.0 | 21.0 | 15750 | 1.1376 | 0.8686 |
| 0.0049 | 22.0 | 16500 | 1.1379 | 0.8686 |
| 0.0014 | 23.0 | 17250 | 1.1542 | 0.8752 |
| 0.0 | 24.0 | 18000 | 1.1536 | 0.8735 |
| 0.0 | 25.0 | 18750 | 1.1721 | 0.8719 |
| 0.0 | 26.0 | 19500 | 1.1498 | 0.8719 |
| 0.01 | 27.0 | 20250 | 1.1595 | 0.8719 |
| 0.0 | 28.0 | 21000 | 1.1250 | 0.8785 |
| 0.0 | 29.0 | 21750 | 1.1514 | 0.8686 |
| 0.0 | 30.0 | 22500 | 1.1182 | 0.8735 |
| 0.0 | 31.0 | 23250 | 1.1637 | 0.8752 |
| 0.0 | 32.0 | 24000 | 1.1726 | 0.8735 |
| 0.0 | 33.0 | 24750 | 1.1697 | 0.8719 |
| 0.0 | 34.0 | 25500 | 1.1588 | 0.8752 |
| 0.0 | 35.0 | 26250 | 1.1653 | 0.8702 |
| 0.0 | 36.0 | 27000 | 1.1669 | 0.8719 |
| 0.0141 | 37.0 | 27750 | 1.1767 | 0.8719 |
| 0.0 | 38.0 | 28500 | 1.1781 | 0.8719 |
| 0.0 | 39.0 | 29250 | 1.1951 | 0.8702 |
| 0.0 | 40.0 | 30000 | 1.1887 | 0.8702 |
| 0.0 | 41.0 | 30750 | 1.1872 | 0.8702 |
| 0.0 | 42.0 | 31500 | 1.1896 | 0.8702 |
| 0.0 | 43.0 | 32250 | 1.1930 | 0.8702 |
| 0.0 | 44.0 | 33000 | 1.1942 | 0.8702 |
| 0.0056 | 45.0 | 33750 | 1.1902 | 0.8702 |
| 0.0 | 46.0 | 34500 | 1.1880 | 0.8702 |
| 0.0 | 47.0 | 35250 | 1.1877 | 0.8702 |
| 0.0 | 48.0 | 36000 | 1.1882 | 0.8702 |
| 0.0 | 49.0 | 36750 | 1.1884 | 0.8702 |
| 0.0 | 50.0 | 37500 | 1.1874 | 0.8719 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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