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---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_base_adamax_00001_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: 0.8818635607321131
---
<!-- 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_base_adamax_00001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7483
- Accuracy: 0.8819
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2908 | 1.0 | 225 | 0.3519 | 0.8469 |
| 0.2412 | 2.0 | 450 | 0.3425 | 0.8652 |
| 0.1561 | 3.0 | 675 | 0.3113 | 0.8752 |
| 0.1722 | 4.0 | 900 | 0.3333 | 0.8819 |
| 0.0793 | 5.0 | 1125 | 0.3397 | 0.8869 |
| 0.0512 | 6.0 | 1350 | 0.3703 | 0.8902 |
| 0.0408 | 7.0 | 1575 | 0.3948 | 0.8869 |
| 0.0153 | 8.0 | 1800 | 0.4474 | 0.8852 |
| 0.0036 | 9.0 | 2025 | 0.5055 | 0.8819 |
| 0.0009 | 10.0 | 2250 | 0.5138 | 0.8918 |
| 0.0011 | 11.0 | 2475 | 0.5776 | 0.8752 |
| 0.0004 | 12.0 | 2700 | 0.6002 | 0.8785 |
| 0.0004 | 13.0 | 2925 | 0.6053 | 0.8819 |
| 0.0002 | 14.0 | 3150 | 0.6097 | 0.8918 |
| 0.0002 | 15.0 | 3375 | 0.6366 | 0.8819 |
| 0.0001 | 16.0 | 3600 | 0.6507 | 0.8819 |
| 0.0042 | 17.0 | 3825 | 0.6732 | 0.8869 |
| 0.0001 | 18.0 | 4050 | 0.6626 | 0.8852 |
| 0.0001 | 19.0 | 4275 | 0.6800 | 0.8885 |
| 0.0001 | 20.0 | 4500 | 0.6886 | 0.8852 |
| 0.0001 | 21.0 | 4725 | 0.7001 | 0.8819 |
| 0.0001 | 22.0 | 4950 | 0.7256 | 0.8869 |
| 0.008 | 23.0 | 5175 | 0.7472 | 0.8918 |
| 0.0001 | 24.0 | 5400 | 0.7160 | 0.8835 |
| 0.0075 | 25.0 | 5625 | 0.7354 | 0.8852 |
| 0.0001 | 26.0 | 5850 | 0.7213 | 0.8819 |
| 0.0001 | 27.0 | 6075 | 0.7101 | 0.8835 |
| 0.0 | 28.0 | 6300 | 0.7245 | 0.8819 |
| 0.0001 | 29.0 | 6525 | 0.7475 | 0.8869 |
| 0.0058 | 30.0 | 6750 | 0.7235 | 0.8852 |
| 0.0 | 31.0 | 6975 | 0.7151 | 0.8852 |
| 0.0 | 32.0 | 7200 | 0.7303 | 0.8852 |
| 0.0 | 33.0 | 7425 | 0.7353 | 0.8835 |
| 0.0 | 34.0 | 7650 | 0.7337 | 0.8835 |
| 0.0 | 35.0 | 7875 | 0.7550 | 0.8835 |
| 0.0034 | 36.0 | 8100 | 0.7409 | 0.8885 |
| 0.0 | 37.0 | 8325 | 0.7323 | 0.8769 |
| 0.0 | 38.0 | 8550 | 0.7381 | 0.8835 |
| 0.0026 | 39.0 | 8775 | 0.7392 | 0.8819 |
| 0.0 | 40.0 | 9000 | 0.7428 | 0.8819 |
| 0.0 | 41.0 | 9225 | 0.7496 | 0.8835 |
| 0.0 | 42.0 | 9450 | 0.7433 | 0.8835 |
| 0.0 | 43.0 | 9675 | 0.7393 | 0.8852 |
| 0.0 | 44.0 | 9900 | 0.7435 | 0.8835 |
| 0.0 | 45.0 | 10125 | 0.7479 | 0.8835 |
| 0.0 | 46.0 | 10350 | 0.7454 | 0.8835 |
| 0.0 | 47.0 | 10575 | 0.7458 | 0.8802 |
| 0.0 | 48.0 | 10800 | 0.7464 | 0.8819 |
| 0.0037 | 49.0 | 11025 | 0.7477 | 0.8835 |
| 0.0037 | 50.0 | 11250 | 0.7483 | 0.8819 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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