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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_fold2
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: 0.7720465890183028
---
<!-- 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_fold2
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: 0.6000
- Accuracy: 0.7720
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1321 | 1.0 | 225 | 1.1948 | 0.4293 |
| 0.871 | 2.0 | 450 | 0.8948 | 0.5175 |
| 0.8008 | 3.0 | 675 | 0.7992 | 0.5408 |
| 0.8238 | 4.0 | 900 | 0.8283 | 0.5574 |
| 0.8152 | 5.0 | 1125 | 0.7604 | 0.6023 |
| 0.7386 | 6.0 | 1350 | 0.8796 | 0.5491 |
| 0.8772 | 7.0 | 1575 | 0.8860 | 0.5874 |
| 0.8376 | 8.0 | 1800 | 0.7510 | 0.6356 |
| 0.7551 | 9.0 | 2025 | 0.8017 | 0.6223 |
| 0.7343 | 10.0 | 2250 | 0.7106 | 0.6506 |
| 0.7921 | 11.0 | 2475 | 0.7927 | 0.6223 |
| 0.7457 | 12.0 | 2700 | 0.6623 | 0.6972 |
| 0.7029 | 13.0 | 2925 | 0.7255 | 0.6855 |
| 0.6991 | 14.0 | 3150 | 0.6397 | 0.7471 |
| 0.6797 | 15.0 | 3375 | 0.6573 | 0.6988 |
| 0.7545 | 16.0 | 3600 | 0.6382 | 0.7038 |
| 0.68 | 17.0 | 3825 | 0.6756 | 0.6805 |
| 0.624 | 18.0 | 4050 | 0.6219 | 0.7488 |
| 0.6708 | 19.0 | 4275 | 0.6554 | 0.7088 |
| 0.6335 | 20.0 | 4500 | 0.6777 | 0.6905 |
| 0.6626 | 21.0 | 4725 | 0.6194 | 0.7438 |
| 0.7083 | 22.0 | 4950 | 0.6072 | 0.7488 |
| 0.7252 | 23.0 | 5175 | 0.5921 | 0.7471 |
| 0.6519 | 24.0 | 5400 | 0.5775 | 0.7454 |
| 0.6375 | 25.0 | 5625 | 0.6257 | 0.7188 |
| 0.6035 | 26.0 | 5850 | 0.5599 | 0.7554 |
| 0.6072 | 27.0 | 6075 | 0.6182 | 0.7338 |
| 0.5614 | 28.0 | 6300 | 0.5694 | 0.7654 |
| 0.5851 | 29.0 | 6525 | 0.5778 | 0.7488 |
| 0.5267 | 30.0 | 6750 | 0.5678 | 0.7521 |
| 0.6187 | 31.0 | 6975 | 0.5810 | 0.7571 |
| 0.5995 | 32.0 | 7200 | 0.5883 | 0.7587 |
| 0.5524 | 33.0 | 7425 | 0.5665 | 0.7671 |
| 0.5635 | 34.0 | 7650 | 0.5545 | 0.7671 |
| 0.5514 | 35.0 | 7875 | 0.5682 | 0.7654 |
| 0.5935 | 36.0 | 8100 | 0.5461 | 0.7687 |
| 0.533 | 37.0 | 8325 | 0.5437 | 0.7820 |
| 0.4461 | 38.0 | 8550 | 0.5819 | 0.7571 |
| 0.4417 | 39.0 | 8775 | 0.5848 | 0.7554 |
| 0.4385 | 40.0 | 9000 | 0.5831 | 0.7754 |
| 0.4422 | 41.0 | 9225 | 0.6218 | 0.7504 |
| 0.5095 | 42.0 | 9450 | 0.5522 | 0.7787 |
| 0.4571 | 43.0 | 9675 | 0.5702 | 0.7854 |
| 0.4352 | 44.0 | 9900 | 0.5650 | 0.7920 |
| 0.4947 | 45.0 | 10125 | 0.6244 | 0.7504 |
| 0.4183 | 46.0 | 10350 | 0.5769 | 0.7754 |
| 0.4309 | 47.0 | 10575 | 0.5669 | 0.7820 |
| 0.441 | 48.0 | 10800 | 0.5895 | 0.7737 |
| 0.475 | 49.0 | 11025 | 0.5930 | 0.7704 |
| 0.4102 | 50.0 | 11250 | 0.6000 | 0.7720 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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