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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_10x_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: 0.8113522537562604
---
<!-- 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_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: 0.4530
- Accuracy: 0.8114
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0181 | 1.0 | 751 | 0.9693 | 0.5359 |
| 0.81 | 2.0 | 1502 | 0.8850 | 0.5993 |
| 0.7699 | 3.0 | 2253 | 0.8246 | 0.6377 |
| 0.6601 | 4.0 | 3004 | 0.7789 | 0.6578 |
| 0.653 | 5.0 | 3755 | 0.7391 | 0.6745 |
| 0.6463 | 6.0 | 4506 | 0.7047 | 0.6912 |
| 0.5744 | 7.0 | 5257 | 0.6756 | 0.7028 |
| 0.4963 | 8.0 | 6008 | 0.6490 | 0.7129 |
| 0.5329 | 9.0 | 6759 | 0.6286 | 0.7195 |
| 0.5165 | 10.0 | 7510 | 0.6094 | 0.7295 |
| 0.5717 | 11.0 | 8261 | 0.5949 | 0.7279 |
| 0.4844 | 12.0 | 9012 | 0.5809 | 0.7396 |
| 0.4587 | 13.0 | 9763 | 0.5699 | 0.7446 |
| 0.4195 | 14.0 | 10514 | 0.5589 | 0.7496 |
| 0.4521 | 15.0 | 11265 | 0.5504 | 0.7579 |
| 0.4327 | 16.0 | 12016 | 0.5411 | 0.7596 |
| 0.4611 | 17.0 | 12767 | 0.5341 | 0.7663 |
| 0.4248 | 18.0 | 13518 | 0.5294 | 0.7746 |
| 0.4694 | 19.0 | 14269 | 0.5215 | 0.7780 |
| 0.395 | 20.0 | 15020 | 0.5170 | 0.7880 |
| 0.3437 | 21.0 | 15771 | 0.5117 | 0.7880 |
| 0.4367 | 22.0 | 16522 | 0.5057 | 0.7947 |
| 0.3451 | 23.0 | 17273 | 0.5010 | 0.7930 |
| 0.4413 | 24.0 | 18024 | 0.4962 | 0.7930 |
| 0.3908 | 25.0 | 18775 | 0.4929 | 0.7930 |
| 0.4631 | 26.0 | 19526 | 0.4899 | 0.7930 |
| 0.3779 | 27.0 | 20277 | 0.4860 | 0.7930 |
| 0.4436 | 28.0 | 21028 | 0.4829 | 0.7963 |
| 0.3794 | 29.0 | 21779 | 0.4792 | 0.7997 |
| 0.3732 | 30.0 | 22530 | 0.4775 | 0.7963 |
| 0.3411 | 31.0 | 23281 | 0.4746 | 0.7980 |
| 0.4745 | 32.0 | 24032 | 0.4718 | 0.7980 |
| 0.4263 | 33.0 | 24783 | 0.4692 | 0.7997 |
| 0.3711 | 34.0 | 25534 | 0.4676 | 0.8030 |
| 0.3951 | 35.0 | 26285 | 0.4656 | 0.8047 |
| 0.4026 | 36.0 | 27036 | 0.4635 | 0.8047 |
| 0.4811 | 37.0 | 27787 | 0.4621 | 0.8063 |
| 0.3816 | 38.0 | 28538 | 0.4609 | 0.8063 |
| 0.2904 | 39.0 | 29289 | 0.4596 | 0.8047 |
| 0.4708 | 40.0 | 30040 | 0.4586 | 0.8097 |
| 0.3633 | 41.0 | 30791 | 0.4575 | 0.8080 |
| 0.367 | 42.0 | 31542 | 0.4565 | 0.8080 |
| 0.4048 | 43.0 | 32293 | 0.4557 | 0.8080 |
| 0.3531 | 44.0 | 33044 | 0.4549 | 0.8080 |
| 0.3608 | 45.0 | 33795 | 0.4542 | 0.8097 |
| 0.3794 | 46.0 | 34546 | 0.4538 | 0.8097 |
| 0.3429 | 47.0 | 35297 | 0.4534 | 0.8114 |
| 0.395 | 48.0 | 36048 | 0.4532 | 0.8114 |
| 0.3682 | 49.0 | 36799 | 0.4531 | 0.8114 |
| 0.3927 | 50.0 | 37550 | 0.4530 | 0.8114 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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