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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_0001_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: 0.7762938230383973
---
<!-- 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_0001_fold1
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: 0.5289
- Accuracy: 0.7763
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.163 | 1.0 | 376 | 1.1394 | 0.3756 |
| 1.1206 | 2.0 | 752 | 1.0830 | 0.4240 |
| 1.0283 | 3.0 | 1128 | 1.0398 | 0.4624 |
| 1.0304 | 4.0 | 1504 | 1.0025 | 0.4925 |
| 0.9327 | 5.0 | 1880 | 0.9675 | 0.5159 |
| 0.9025 | 6.0 | 2256 | 0.9343 | 0.5342 |
| 0.813 | 7.0 | 2632 | 0.9040 | 0.5492 |
| 0.8373 | 8.0 | 3008 | 0.8750 | 0.5793 |
| 0.8156 | 9.0 | 3384 | 0.8442 | 0.6093 |
| 0.7741 | 10.0 | 3760 | 0.8156 | 0.6344 |
| 0.6787 | 11.0 | 4136 | 0.7888 | 0.6477 |
| 0.6914 | 12.0 | 4512 | 0.7651 | 0.6561 |
| 0.6673 | 13.0 | 4888 | 0.7424 | 0.6761 |
| 0.6471 | 14.0 | 5264 | 0.7219 | 0.6895 |
| 0.5827 | 15.0 | 5640 | 0.7043 | 0.6978 |
| 0.5679 | 16.0 | 6016 | 0.6891 | 0.7062 |
| 0.5387 | 17.0 | 6392 | 0.6730 | 0.7095 |
| 0.5827 | 18.0 | 6768 | 0.6602 | 0.7145 |
| 0.5764 | 19.0 | 7144 | 0.6481 | 0.7179 |
| 0.5667 | 20.0 | 7520 | 0.6375 | 0.7312 |
| 0.5598 | 21.0 | 7896 | 0.6271 | 0.7329 |
| 0.4963 | 22.0 | 8272 | 0.6181 | 0.7346 |
| 0.5399 | 23.0 | 8648 | 0.6097 | 0.7396 |
| 0.6005 | 24.0 | 9024 | 0.6025 | 0.7429 |
| 0.5535 | 25.0 | 9400 | 0.5952 | 0.7479 |
| 0.5292 | 26.0 | 9776 | 0.5886 | 0.7513 |
| 0.4834 | 27.0 | 10152 | 0.5826 | 0.7529 |
| 0.4735 | 28.0 | 10528 | 0.5772 | 0.7546 |
| 0.5034 | 29.0 | 10904 | 0.5722 | 0.7563 |
| 0.4846 | 30.0 | 11280 | 0.5675 | 0.7579 |
| 0.5398 | 31.0 | 11656 | 0.5634 | 0.7596 |
| 0.5623 | 32.0 | 12032 | 0.5594 | 0.7646 |
| 0.5122 | 33.0 | 12408 | 0.5557 | 0.7646 |
| 0.483 | 34.0 | 12784 | 0.5523 | 0.7663 |
| 0.4676 | 35.0 | 13160 | 0.5492 | 0.7663 |
| 0.4655 | 36.0 | 13536 | 0.5464 | 0.7679 |
| 0.4231 | 37.0 | 13912 | 0.5438 | 0.7663 |
| 0.5103 | 38.0 | 14288 | 0.5415 | 0.7663 |
| 0.4626 | 39.0 | 14664 | 0.5394 | 0.7679 |
| 0.4372 | 40.0 | 15040 | 0.5375 | 0.7679 |
| 0.496 | 41.0 | 15416 | 0.5357 | 0.7713 |
| 0.4002 | 42.0 | 15792 | 0.5343 | 0.7746 |
| 0.4506 | 43.0 | 16168 | 0.5329 | 0.7746 |
| 0.472 | 44.0 | 16544 | 0.5318 | 0.7746 |
| 0.467 | 45.0 | 16920 | 0.5309 | 0.7763 |
| 0.4861 | 46.0 | 17296 | 0.5301 | 0.7746 |
| 0.4576 | 47.0 | 17672 | 0.5296 | 0.7746 |
| 0.4597 | 48.0 | 18048 | 0.5292 | 0.7763 |
| 0.4465 | 49.0 | 18424 | 0.5290 | 0.7763 |
| 0.3972 | 50.0 | 18800 | 0.5289 | 0.7763 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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