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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_10x_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: 0.8063439065108514
---
<!-- 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_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: 1.5007
- Accuracy: 0.8063
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7661 | 1.0 | 751 | 0.8852 | 0.6060 |
| 0.6485 | 2.0 | 1502 | 0.7308 | 0.6578 |
| 0.696 | 3.0 | 2253 | 0.7036 | 0.6594 |
| 0.6301 | 4.0 | 3004 | 0.7247 | 0.6761 |
| 0.6536 | 5.0 | 3755 | 0.6760 | 0.6828 |
| 0.6653 | 6.0 | 4506 | 0.6159 | 0.7095 |
| 0.5636 | 7.0 | 5257 | 0.5571 | 0.7579 |
| 0.5506 | 8.0 | 6008 | 0.6121 | 0.7329 |
| 0.5582 | 9.0 | 6759 | 0.5862 | 0.7546 |
| 0.5548 | 10.0 | 7510 | 0.5892 | 0.7329 |
| 0.5549 | 11.0 | 8261 | 0.5848 | 0.7412 |
| 0.5362 | 12.0 | 9012 | 0.6200 | 0.7396 |
| 0.4966 | 13.0 | 9763 | 0.5530 | 0.7713 |
| 0.4818 | 14.0 | 10514 | 0.5786 | 0.7529 |
| 0.4746 | 15.0 | 11265 | 0.6115 | 0.7229 |
| 0.4852 | 16.0 | 12016 | 0.6019 | 0.7362 |
| 0.4634 | 17.0 | 12767 | 0.5783 | 0.7613 |
| 0.453 | 18.0 | 13518 | 0.5821 | 0.7462 |
| 0.4908 | 19.0 | 14269 | 0.5445 | 0.7629 |
| 0.4881 | 20.0 | 15020 | 0.5377 | 0.7763 |
| 0.4025 | 21.0 | 15771 | 0.5423 | 0.7813 |
| 0.4591 | 22.0 | 16522 | 0.5168 | 0.7813 |
| 0.3695 | 23.0 | 17273 | 0.5306 | 0.7730 |
| 0.4288 | 24.0 | 18024 | 0.5369 | 0.7997 |
| 0.4022 | 25.0 | 18775 | 0.5176 | 0.7896 |
| 0.3916 | 26.0 | 19526 | 0.5681 | 0.7830 |
| 0.4188 | 27.0 | 20277 | 0.5488 | 0.7830 |
| 0.4088 | 28.0 | 21028 | 0.5430 | 0.7947 |
| 0.3236 | 29.0 | 21779 | 0.5528 | 0.7947 |
| 0.3272 | 30.0 | 22530 | 0.5104 | 0.8164 |
| 0.305 | 31.0 | 23281 | 0.5401 | 0.8080 |
| 0.3925 | 32.0 | 24032 | 0.5133 | 0.8013 |
| 0.3211 | 33.0 | 24783 | 0.5292 | 0.7980 |
| 0.2648 | 34.0 | 25534 | 0.6583 | 0.7846 |
| 0.2286 | 35.0 | 26285 | 0.6241 | 0.7896 |
| 0.2863 | 36.0 | 27036 | 0.6657 | 0.7947 |
| 0.2968 | 37.0 | 27787 | 0.5922 | 0.8214 |
| 0.2233 | 38.0 | 28538 | 0.6706 | 0.7880 |
| 0.1424 | 39.0 | 29289 | 0.6769 | 0.8097 |
| 0.2253 | 40.0 | 30040 | 0.7552 | 0.7963 |
| 0.1253 | 41.0 | 30791 | 0.7804 | 0.8164 |
| 0.16 | 42.0 | 31542 | 0.8311 | 0.7980 |
| 0.1962 | 43.0 | 32293 | 0.8198 | 0.8047 |
| 0.0759 | 44.0 | 33044 | 0.9444 | 0.7997 |
| 0.1175 | 45.0 | 33795 | 0.9448 | 0.8080 |
| 0.1291 | 46.0 | 34546 | 1.0860 | 0.8080 |
| 0.0879 | 47.0 | 35297 | 1.2492 | 0.7980 |
| 0.0404 | 48.0 | 36048 | 1.3416 | 0.8047 |
| 0.0466 | 49.0 | 36799 | 1.4861 | 0.8030 |
| 0.0362 | 50.0 | 37550 | 1.5007 | 0.8063 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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