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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_5x_deit_tiny_sgd_001_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: 0.8716666666666667
---
<!-- 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_5x_deit_tiny_sgd_001_fold5
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: 0.2848
- Accuracy: 0.8717
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7235 | 1.0 | 375 | 0.7207 | 0.7267 |
| 0.5565 | 2.0 | 750 | 0.5409 | 0.7917 |
| 0.4119 | 3.0 | 1125 | 0.4652 | 0.825 |
| 0.4334 | 4.0 | 1500 | 0.4261 | 0.825 |
| 0.3887 | 5.0 | 1875 | 0.3995 | 0.8317 |
| 0.3453 | 6.0 | 2250 | 0.3796 | 0.8483 |
| 0.3126 | 7.0 | 2625 | 0.3666 | 0.8533 |
| 0.2904 | 8.0 | 3000 | 0.3555 | 0.8533 |
| 0.2918 | 9.0 | 3375 | 0.3426 | 0.8617 |
| 0.2742 | 10.0 | 3750 | 0.3359 | 0.8583 |
| 0.2643 | 11.0 | 4125 | 0.3302 | 0.865 |
| 0.2302 | 12.0 | 4500 | 0.3258 | 0.8617 |
| 0.2469 | 13.0 | 4875 | 0.3202 | 0.8633 |
| 0.2562 | 14.0 | 5250 | 0.3189 | 0.8683 |
| 0.29 | 15.0 | 5625 | 0.3116 | 0.87 |
| 0.1812 | 16.0 | 6000 | 0.3053 | 0.87 |
| 0.297 | 17.0 | 6375 | 0.3040 | 0.8733 |
| 0.2383 | 18.0 | 6750 | 0.3060 | 0.8733 |
| 0.2162 | 19.0 | 7125 | 0.3016 | 0.8683 |
| 0.1713 | 20.0 | 7500 | 0.3023 | 0.8717 |
| 0.1959 | 21.0 | 7875 | 0.2972 | 0.8767 |
| 0.1937 | 22.0 | 8250 | 0.2967 | 0.87 |
| 0.2843 | 23.0 | 8625 | 0.2932 | 0.8683 |
| 0.199 | 24.0 | 9000 | 0.2945 | 0.8717 |
| 0.2226 | 25.0 | 9375 | 0.2906 | 0.875 |
| 0.2091 | 26.0 | 9750 | 0.2867 | 0.8683 |
| 0.2068 | 27.0 | 10125 | 0.2936 | 0.8717 |
| 0.1892 | 28.0 | 10500 | 0.2828 | 0.8767 |
| 0.2214 | 29.0 | 10875 | 0.2884 | 0.8683 |
| 0.1748 | 30.0 | 11250 | 0.2926 | 0.8683 |
| 0.2363 | 31.0 | 11625 | 0.2864 | 0.8733 |
| 0.209 | 32.0 | 12000 | 0.2852 | 0.8733 |
| 0.2053 | 33.0 | 12375 | 0.2863 | 0.8683 |
| 0.2387 | 34.0 | 12750 | 0.2806 | 0.8767 |
| 0.1656 | 35.0 | 13125 | 0.2863 | 0.87 |
| 0.1491 | 36.0 | 13500 | 0.2870 | 0.8667 |
| 0.1628 | 37.0 | 13875 | 0.2841 | 0.87 |
| 0.1814 | 38.0 | 14250 | 0.2851 | 0.8683 |
| 0.1769 | 39.0 | 14625 | 0.2852 | 0.8667 |
| 0.1855 | 40.0 | 15000 | 0.2843 | 0.8717 |
| 0.185 | 41.0 | 15375 | 0.2824 | 0.8717 |
| 0.1482 | 42.0 | 15750 | 0.2829 | 0.8733 |
| 0.2112 | 43.0 | 16125 | 0.2835 | 0.875 |
| 0.2011 | 44.0 | 16500 | 0.2835 | 0.8733 |
| 0.1775 | 45.0 | 16875 | 0.2835 | 0.8717 |
| 0.1816 | 46.0 | 17250 | 0.2845 | 0.8717 |
| 0.1826 | 47.0 | 17625 | 0.2843 | 0.8717 |
| 0.1411 | 48.0 | 18000 | 0.2851 | 0.8683 |
| 0.1734 | 49.0 | 18375 | 0.2850 | 0.8717 |
| 0.1888 | 50.0 | 18750 | 0.2848 | 0.8717 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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