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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_3x_deit_small_rms_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: 0.7662771285475793
---
<!-- 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_3x_deit_small_rms_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: 1.0295
- Accuracy: 0.7663
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9037 | 1.0 | 226 | 1.1611 | 0.4224 |
| 0.8436 | 2.0 | 452 | 0.8419 | 0.5442 |
| 0.8202 | 3.0 | 678 | 0.8414 | 0.5359 |
| 0.8734 | 4.0 | 904 | 0.8332 | 0.5326 |
| 0.8282 | 5.0 | 1130 | 0.7907 | 0.6127 |
| 0.8721 | 6.0 | 1356 | 0.8061 | 0.5559 |
| 0.7744 | 7.0 | 1582 | 0.7612 | 0.6260 |
| 0.7444 | 8.0 | 1808 | 0.8606 | 0.5492 |
| 0.7266 | 9.0 | 2034 | 0.7492 | 0.6427 |
| 0.7385 | 10.0 | 2260 | 0.7643 | 0.6344 |
| 0.6851 | 11.0 | 2486 | 0.7983 | 0.5843 |
| 0.6844 | 12.0 | 2712 | 0.7946 | 0.6561 |
| 0.6727 | 13.0 | 2938 | 0.8087 | 0.6244 |
| 0.6244 | 14.0 | 3164 | 0.6709 | 0.6912 |
| 0.6712 | 15.0 | 3390 | 0.6742 | 0.7095 |
| 0.6346 | 16.0 | 3616 | 0.6684 | 0.7162 |
| 0.5408 | 17.0 | 3842 | 0.6615 | 0.7028 |
| 0.63 | 18.0 | 4068 | 0.6480 | 0.7295 |
| 0.6263 | 19.0 | 4294 | 0.7205 | 0.6611 |
| 0.5327 | 20.0 | 4520 | 0.6519 | 0.7078 |
| 0.6622 | 21.0 | 4746 | 0.6350 | 0.7179 |
| 0.6299 | 22.0 | 4972 | 0.8817 | 0.6210 |
| 0.6304 | 23.0 | 5198 | 0.6476 | 0.7362 |
| 0.5526 | 24.0 | 5424 | 0.6677 | 0.7145 |
| 0.6295 | 25.0 | 5650 | 0.6118 | 0.7546 |
| 0.6308 | 26.0 | 5876 | 0.6212 | 0.7362 |
| 0.5383 | 27.0 | 6102 | 0.7015 | 0.7179 |
| 0.5618 | 28.0 | 6328 | 0.8218 | 0.6711 |
| 0.4879 | 29.0 | 6554 | 0.7043 | 0.6928 |
| 0.5827 | 30.0 | 6780 | 0.6552 | 0.7229 |
| 0.5364 | 31.0 | 7006 | 0.6340 | 0.7379 |
| 0.4905 | 32.0 | 7232 | 0.6047 | 0.7529 |
| 0.4492 | 33.0 | 7458 | 0.7039 | 0.7028 |
| 0.4914 | 34.0 | 7684 | 0.6660 | 0.7379 |
| 0.3519 | 35.0 | 7910 | 0.6494 | 0.7479 |
| 0.3791 | 36.0 | 8136 | 0.6497 | 0.7513 |
| 0.4111 | 37.0 | 8362 | 0.6075 | 0.7646 |
| 0.4433 | 38.0 | 8588 | 0.6728 | 0.7679 |
| 0.3357 | 39.0 | 8814 | 0.6576 | 0.7529 |
| 0.3901 | 40.0 | 9040 | 0.6972 | 0.7596 |
| 0.4094 | 41.0 | 9266 | 0.6481 | 0.7696 |
| 0.3576 | 42.0 | 9492 | 0.6871 | 0.7746 |
| 0.335 | 43.0 | 9718 | 0.7307 | 0.7846 |
| 0.2737 | 44.0 | 9944 | 0.7687 | 0.7746 |
| 0.3485 | 45.0 | 10170 | 0.7785 | 0.7780 |
| 0.278 | 46.0 | 10396 | 0.8580 | 0.7730 |
| 0.2622 | 47.0 | 10622 | 0.8921 | 0.7713 |
| 0.2496 | 48.0 | 10848 | 0.9544 | 0.7730 |
| 0.1441 | 49.0 | 11074 | 0.9744 | 0.7730 |
| 0.1894 | 50.0 | 11300 | 1.0295 | 0.7663 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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