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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_sgd_001_fold1
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: 0.9015025041736227
---
<!-- 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_sgd_001_fold1
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: 0.2862
- Accuracy: 0.9015
## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5539 | 1.0 | 751 | 0.5690 | 0.7763 |
| 0.3867 | 2.0 | 1502 | 0.4456 | 0.8314 |
| 0.3236 | 3.0 | 2253 | 0.3927 | 0.8497 |
| 0.259 | 4.0 | 3004 | 0.3726 | 0.8514 |
| 0.3099 | 5.0 | 3755 | 0.3487 | 0.8598 |
| 0.2986 | 6.0 | 4506 | 0.3416 | 0.8715 |
| 0.2728 | 7.0 | 5257 | 0.3260 | 0.8731 |
| 0.2249 | 8.0 | 6008 | 0.3188 | 0.8781 |
| 0.2673 | 9.0 | 6759 | 0.3155 | 0.8848 |
| 0.2491 | 10.0 | 7510 | 0.3089 | 0.8848 |
| 0.2349 | 11.0 | 8261 | 0.3099 | 0.8881 |
| 0.2513 | 12.0 | 9012 | 0.3016 | 0.8898 |
| 0.2098 | 13.0 | 9763 | 0.3061 | 0.8898 |
| 0.1606 | 14.0 | 10514 | 0.3022 | 0.8881 |
| 0.1914 | 15.0 | 11265 | 0.2955 | 0.8881 |
| 0.2039 | 16.0 | 12016 | 0.2953 | 0.8898 |
| 0.2821 | 17.0 | 12767 | 0.2940 | 0.8965 |
| 0.1703 | 18.0 | 13518 | 0.2962 | 0.8915 |
| 0.2178 | 19.0 | 14269 | 0.2905 | 0.8965 |
| 0.1883 | 20.0 | 15020 | 0.2902 | 0.8998 |
| 0.13 | 21.0 | 15771 | 0.2893 | 0.8948 |
| 0.1613 | 22.0 | 16522 | 0.2875 | 0.8982 |
| 0.1627 | 23.0 | 17273 | 0.2879 | 0.8948 |
| 0.2201 | 24.0 | 18024 | 0.2853 | 0.8998 |
| 0.2067 | 25.0 | 18775 | 0.2893 | 0.8965 |
| 0.1982 | 26.0 | 19526 | 0.2860 | 0.8982 |
| 0.1922 | 27.0 | 20277 | 0.2854 | 0.8998 |
| 0.2065 | 28.0 | 21028 | 0.2873 | 0.8948 |
| 0.1663 | 29.0 | 21779 | 0.2836 | 0.9032 |
| 0.1637 | 30.0 | 22530 | 0.2824 | 0.9032 |
| 0.1216 | 31.0 | 23281 | 0.2840 | 0.8998 |
| 0.2073 | 32.0 | 24032 | 0.2863 | 0.9065 |
| 0.1694 | 33.0 | 24783 | 0.2888 | 0.8965 |
| 0.1525 | 34.0 | 25534 | 0.2882 | 0.8982 |
| 0.1562 | 35.0 | 26285 | 0.2864 | 0.9032 |
| 0.1612 | 36.0 | 27036 | 0.2821 | 0.9032 |
| 0.2418 | 37.0 | 27787 | 0.2832 | 0.9015 |
| 0.138 | 38.0 | 28538 | 0.2859 | 0.9032 |
| 0.0832 | 39.0 | 29289 | 0.2853 | 0.8998 |
| 0.1792 | 40.0 | 30040 | 0.2866 | 0.9015 |
| 0.1296 | 41.0 | 30791 | 0.2848 | 0.9032 |
| 0.1436 | 42.0 | 31542 | 0.2863 | 0.9032 |
| 0.1676 | 43.0 | 32293 | 0.2864 | 0.9015 |
| 0.129 | 44.0 | 33044 | 0.2863 | 0.9015 |
| 0.1268 | 45.0 | 33795 | 0.2864 | 0.9015 |
| 0.182 | 46.0 | 34546 | 0.2870 | 0.8998 |
| 0.0802 | 47.0 | 35297 | 0.2872 | 0.9015 |
| 0.1369 | 48.0 | 36048 | 0.2866 | 0.9015 |
| 0.1294 | 49.0 | 36799 | 0.2861 | 0.9015 |
| 0.1488 | 50.0 | 37550 | 0.2862 | 0.9015 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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