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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_fold3
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: 0.7766666666666666
---
<!-- 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_fold3
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: 0.5590
- Accuracy: 0.7767
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8839 | 1.0 | 750 | 0.8956 | 0.4917 |
| 0.8402 | 2.0 | 1500 | 0.8459 | 0.5383 |
| 0.827 | 3.0 | 2250 | 0.8365 | 0.5417 |
| 0.7595 | 4.0 | 3000 | 0.8404 | 0.5617 |
| 0.8496 | 5.0 | 3750 | 0.9112 | 0.505 |
| 0.7825 | 6.0 | 4500 | 0.8246 | 0.6233 |
| 0.8185 | 7.0 | 5250 | 0.7843 | 0.6233 |
| 0.7863 | 8.0 | 6000 | 0.7862 | 0.6183 |
| 0.7304 | 9.0 | 6750 | 0.7478 | 0.6433 |
| 0.7486 | 10.0 | 7500 | 0.7941 | 0.625 |
| 0.7979 | 11.0 | 8250 | 0.7438 | 0.6817 |
| 0.6928 | 12.0 | 9000 | 0.8898 | 0.58 |
| 0.683 | 13.0 | 9750 | 0.7126 | 0.68 |
| 0.7194 | 14.0 | 10500 | 0.7634 | 0.6367 |
| 0.7001 | 15.0 | 11250 | 0.6906 | 0.68 |
| 0.7209 | 16.0 | 12000 | 0.6988 | 0.675 |
| 0.693 | 17.0 | 12750 | 0.7227 | 0.6733 |
| 0.6594 | 18.0 | 13500 | 0.7119 | 0.675 |
| 0.6733 | 19.0 | 14250 | 0.6769 | 0.695 |
| 0.6368 | 20.0 | 15000 | 0.6310 | 0.7183 |
| 0.5529 | 21.0 | 15750 | 0.6379 | 0.73 |
| 0.674 | 22.0 | 16500 | 0.6200 | 0.7233 |
| 0.6173 | 23.0 | 17250 | 0.6390 | 0.7117 |
| 0.7017 | 24.0 | 18000 | 0.6234 | 0.7217 |
| 0.6672 | 25.0 | 18750 | 0.6159 | 0.7117 |
| 0.6143 | 26.0 | 19500 | 0.6119 | 0.7133 |
| 0.5447 | 27.0 | 20250 | 0.6511 | 0.7 |
| 0.616 | 28.0 | 21000 | 0.5943 | 0.7317 |
| 0.6257 | 29.0 | 21750 | 0.6135 | 0.7417 |
| 0.5784 | 30.0 | 22500 | 0.6236 | 0.7383 |
| 0.5488 | 31.0 | 23250 | 0.5814 | 0.7483 |
| 0.5683 | 32.0 | 24000 | 0.6409 | 0.725 |
| 0.5657 | 33.0 | 24750 | 0.6193 | 0.7583 |
| 0.7061 | 34.0 | 25500 | 0.7958 | 0.6533 |
| 0.5815 | 35.0 | 26250 | 0.6092 | 0.7467 |
| 0.545 | 36.0 | 27000 | 0.5902 | 0.7567 |
| 0.574 | 37.0 | 27750 | 0.5865 | 0.7483 |
| 0.5654 | 38.0 | 28500 | 0.6161 | 0.7467 |
| 0.5393 | 39.0 | 29250 | 0.5677 | 0.7667 |
| 0.6213 | 40.0 | 30000 | 0.5702 | 0.7633 |
| 0.5565 | 41.0 | 30750 | 0.5675 | 0.75 |
| 0.5323 | 42.0 | 31500 | 0.5645 | 0.7583 |
| 0.5444 | 43.0 | 32250 | 0.5820 | 0.76 |
| 0.4988 | 44.0 | 33000 | 0.5588 | 0.765 |
| 0.5249 | 45.0 | 33750 | 0.5669 | 0.7583 |
| 0.5246 | 46.0 | 34500 | 0.5504 | 0.7733 |
| 0.4975 | 47.0 | 35250 | 0.5697 | 0.7717 |
| 0.5083 | 48.0 | 36000 | 0.5554 | 0.7717 |
| 0.4948 | 49.0 | 36750 | 0.5551 | 0.775 |
| 0.4147 | 50.0 | 37500 | 0.5590 | 0.7767 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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