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---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_001_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: 0.9
---
<!-- 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_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2648
- Accuracy: 0.9
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7316 | 1.0 | 375 | 0.7849 | 0.6383 |
| 0.5408 | 2.0 | 750 | 0.5416 | 0.805 |
| 0.4144 | 3.0 | 1125 | 0.4477 | 0.8483 |
| 0.4149 | 4.0 | 1500 | 0.3929 | 0.8533 |
| 0.413 | 5.0 | 1875 | 0.3596 | 0.865 |
| 0.347 | 6.0 | 2250 | 0.3405 | 0.87 |
| 0.3766 | 7.0 | 2625 | 0.3237 | 0.885 |
| 0.3353 | 8.0 | 3000 | 0.3140 | 0.8833 |
| 0.2912 | 9.0 | 3375 | 0.3069 | 0.8817 |
| 0.2983 | 10.0 | 3750 | 0.3022 | 0.8883 |
| 0.2271 | 11.0 | 4125 | 0.2992 | 0.8867 |
| 0.2804 | 12.0 | 4500 | 0.2892 | 0.8917 |
| 0.2434 | 13.0 | 4875 | 0.2876 | 0.89 |
| 0.2434 | 14.0 | 5250 | 0.2819 | 0.8883 |
| 0.2457 | 15.0 | 5625 | 0.2817 | 0.8967 |
| 0.2178 | 16.0 | 6000 | 0.2772 | 0.9 |
| 0.2586 | 17.0 | 6375 | 0.2753 | 0.9 |
| 0.2424 | 18.0 | 6750 | 0.2760 | 0.8967 |
| 0.2316 | 19.0 | 7125 | 0.2730 | 0.8967 |
| 0.236 | 20.0 | 7500 | 0.2701 | 0.9033 |
| 0.1785 | 21.0 | 7875 | 0.2679 | 0.9017 |
| 0.1868 | 22.0 | 8250 | 0.2698 | 0.9 |
| 0.2515 | 23.0 | 8625 | 0.2683 | 0.8983 |
| 0.2504 | 24.0 | 9000 | 0.2635 | 0.8967 |
| 0.2044 | 25.0 | 9375 | 0.2645 | 0.9033 |
| 0.2051 | 26.0 | 9750 | 0.2668 | 0.8983 |
| 0.2231 | 27.0 | 10125 | 0.2645 | 0.9033 |
| 0.2003 | 28.0 | 10500 | 0.2627 | 0.8983 |
| 0.1423 | 29.0 | 10875 | 0.2631 | 0.9033 |
| 0.2099 | 30.0 | 11250 | 0.2641 | 0.9 |
| 0.2023 | 31.0 | 11625 | 0.2642 | 0.9 |
| 0.2174 | 32.0 | 12000 | 0.2642 | 0.9 |
| 0.198 | 33.0 | 12375 | 0.2636 | 0.8967 |
| 0.1518 | 34.0 | 12750 | 0.2625 | 0.9033 |
| 0.1375 | 35.0 | 13125 | 0.2629 | 0.9017 |
| 0.1414 | 36.0 | 13500 | 0.2638 | 0.9017 |
| 0.1599 | 37.0 | 13875 | 0.2634 | 0.9033 |
| 0.164 | 38.0 | 14250 | 0.2642 | 0.9 |
| 0.1442 | 39.0 | 14625 | 0.2626 | 0.8983 |
| 0.1928 | 40.0 | 15000 | 0.2641 | 0.9017 |
| 0.1643 | 41.0 | 15375 | 0.2643 | 0.9017 |
| 0.1534 | 42.0 | 15750 | 0.2642 | 0.9017 |
| 0.1818 | 43.0 | 16125 | 0.2644 | 0.9017 |
| 0.1596 | 44.0 | 16500 | 0.2650 | 0.9 |
| 0.1441 | 45.0 | 16875 | 0.2645 | 0.9017 |
| 0.1513 | 46.0 | 17250 | 0.2643 | 0.9 |
| 0.1221 | 47.0 | 17625 | 0.2647 | 0.9 |
| 0.1853 | 48.0 | 18000 | 0.2646 | 0.9 |
| 0.1404 | 49.0 | 18375 | 0.2649 | 0.9 |
| 0.1644 | 50.0 | 18750 | 0.2648 | 0.9 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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