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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_1x_deit_tiny_adamax_001_fold4
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: 0.84
---
<!-- 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_1x_deit_tiny_adamax_001_fold4
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: 1.6471
- Accuracy: 0.84
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7464 | 1.0 | 75 | 0.5642 | 0.795 |
| 0.5514 | 2.0 | 150 | 0.5108 | 0.8017 |
| 0.3856 | 3.0 | 225 | 0.5385 | 0.8033 |
| 0.4541 | 4.0 | 300 | 0.4488 | 0.825 |
| 0.4047 | 5.0 | 375 | 0.4448 | 0.815 |
| 0.2699 | 6.0 | 450 | 0.5321 | 0.8267 |
| 0.3444 | 7.0 | 525 | 0.4477 | 0.8367 |
| 0.1984 | 8.0 | 600 | 0.5491 | 0.81 |
| 0.1797 | 9.0 | 675 | 0.7263 | 0.8167 |
| 0.1145 | 10.0 | 750 | 0.6218 | 0.8317 |
| 0.1353 | 11.0 | 825 | 0.7800 | 0.8183 |
| 0.1658 | 12.0 | 900 | 0.6252 | 0.835 |
| 0.101 | 13.0 | 975 | 0.8640 | 0.805 |
| 0.0897 | 14.0 | 1050 | 0.9357 | 0.8 |
| 0.0267 | 15.0 | 1125 | 1.0487 | 0.8283 |
| 0.0597 | 16.0 | 1200 | 1.0545 | 0.8283 |
| 0.0984 | 17.0 | 1275 | 0.9221 | 0.83 |
| 0.0994 | 18.0 | 1350 | 0.9468 | 0.8367 |
| 0.0261 | 19.0 | 1425 | 1.1404 | 0.8117 |
| 0.0439 | 20.0 | 1500 | 1.1737 | 0.8233 |
| 0.0258 | 21.0 | 1575 | 1.1898 | 0.8383 |
| 0.0027 | 22.0 | 1650 | 1.4604 | 0.8217 |
| 0.0296 | 23.0 | 1725 | 1.3681 | 0.8267 |
| 0.004 | 24.0 | 1800 | 1.5826 | 0.83 |
| 0.0296 | 25.0 | 1875 | 1.2731 | 0.8167 |
| 0.0022 | 26.0 | 1950 | 1.4166 | 0.83 |
| 0.0213 | 27.0 | 2025 | 1.3755 | 0.8433 |
| 0.0191 | 28.0 | 2100 | 1.6417 | 0.82 |
| 0.0068 | 29.0 | 2175 | 1.3938 | 0.8417 |
| 0.0003 | 30.0 | 2250 | 1.4213 | 0.8317 |
| 0.0002 | 31.0 | 2325 | 1.4622 | 0.8417 |
| 0.0 | 32.0 | 2400 | 1.5110 | 0.8367 |
| 0.0291 | 33.0 | 2475 | 1.4845 | 0.8383 |
| 0.0005 | 34.0 | 2550 | 1.5757 | 0.8333 |
| 0.0089 | 35.0 | 2625 | 1.6525 | 0.83 |
| 0.0053 | 36.0 | 2700 | 1.6166 | 0.84 |
| 0.0078 | 37.0 | 2775 | 1.5899 | 0.8467 |
| 0.0 | 38.0 | 2850 | 1.6250 | 0.8433 |
| 0.0004 | 39.0 | 2925 | 1.6311 | 0.8433 |
| 0.0 | 40.0 | 3000 | 1.6268 | 0.8433 |
| 0.0032 | 41.0 | 3075 | 1.6310 | 0.8417 |
| 0.0 | 42.0 | 3150 | 1.6322 | 0.84 |
| 0.0 | 43.0 | 3225 | 1.6387 | 0.84 |
| 0.0 | 44.0 | 3300 | 1.6405 | 0.84 |
| 0.0 | 45.0 | 3375 | 1.6426 | 0.84 |
| 0.0 | 46.0 | 3450 | 1.6435 | 0.84 |
| 0.0 | 47.0 | 3525 | 1.6443 | 0.84 |
| 0.0 | 48.0 | 3600 | 1.6452 | 0.84 |
| 0.0 | 49.0 | 3675 | 1.6465 | 0.84 |
| 0.0 | 50.0 | 3750 | 1.6471 | 0.84 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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