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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_5x_deit_base_adamax_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: 0.89
---
<!-- 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_5x_deit_base_adamax_001_fold3
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: 1.1005
- Accuracy: 0.89
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.307 | 1.0 | 375 | 0.3243 | 0.88 |
| 0.1951 | 2.0 | 750 | 0.2848 | 0.8967 |
| 0.1756 | 3.0 | 1125 | 0.3260 | 0.8767 |
| 0.1301 | 4.0 | 1500 | 0.3461 | 0.8933 |
| 0.1724 | 5.0 | 1875 | 0.3433 | 0.8783 |
| 0.1105 | 6.0 | 2250 | 0.5327 | 0.8517 |
| 0.105 | 7.0 | 2625 | 0.4495 | 0.89 |
| 0.1373 | 8.0 | 3000 | 0.3477 | 0.8933 |
| 0.0545 | 9.0 | 3375 | 0.5403 | 0.8767 |
| 0.026 | 10.0 | 3750 | 0.6392 | 0.8717 |
| 0.0547 | 11.0 | 4125 | 0.6160 | 0.875 |
| 0.0385 | 12.0 | 4500 | 0.5572 | 0.885 |
| 0.0376 | 13.0 | 4875 | 0.6146 | 0.8967 |
| 0.0031 | 14.0 | 5250 | 0.6509 | 0.8883 |
| 0.0185 | 15.0 | 5625 | 0.6515 | 0.885 |
| 0.0353 | 16.0 | 6000 | 0.7637 | 0.885 |
| 0.0052 | 17.0 | 6375 | 0.7211 | 0.8817 |
| 0.011 | 18.0 | 6750 | 0.5915 | 0.9067 |
| 0.0053 | 19.0 | 7125 | 0.6576 | 0.89 |
| 0.0044 | 20.0 | 7500 | 0.6728 | 0.8983 |
| 0.0003 | 21.0 | 7875 | 0.7362 | 0.8817 |
| 0.0001 | 22.0 | 8250 | 0.7370 | 0.8817 |
| 0.0265 | 23.0 | 8625 | 0.6954 | 0.895 |
| 0.0011 | 24.0 | 9000 | 0.7244 | 0.8883 |
| 0.0056 | 25.0 | 9375 | 0.7383 | 0.8917 |
| 0.0 | 26.0 | 9750 | 0.6944 | 0.9033 |
| 0.0001 | 27.0 | 10125 | 0.8581 | 0.8933 |
| 0.0002 | 28.0 | 10500 | 0.7732 | 0.8917 |
| 0.0001 | 29.0 | 10875 | 0.9540 | 0.8867 |
| 0.005 | 30.0 | 11250 | 0.8145 | 0.8933 |
| 0.0003 | 31.0 | 11625 | 0.8223 | 0.8967 |
| 0.0 | 32.0 | 12000 | 0.8225 | 0.89 |
| 0.0 | 33.0 | 12375 | 0.8479 | 0.8933 |
| 0.0 | 34.0 | 12750 | 0.8571 | 0.895 |
| 0.0 | 35.0 | 13125 | 0.9119 | 0.8917 |
| 0.0 | 36.0 | 13500 | 0.9029 | 0.8917 |
| 0.0 | 37.0 | 13875 | 0.9226 | 0.8967 |
| 0.0 | 38.0 | 14250 | 0.9083 | 0.895 |
| 0.0 | 39.0 | 14625 | 1.0048 | 0.8933 |
| 0.0026 | 40.0 | 15000 | 1.0018 | 0.8883 |
| 0.0 | 41.0 | 15375 | 1.0177 | 0.8917 |
| 0.0 | 42.0 | 15750 | 1.0273 | 0.8917 |
| 0.0 | 43.0 | 16125 | 1.0393 | 0.8933 |
| 0.0 | 44.0 | 16500 | 1.0649 | 0.895 |
| 0.0 | 45.0 | 16875 | 1.0825 | 0.8883 |
| 0.0 | 46.0 | 17250 | 1.0743 | 0.895 |
| 0.0 | 47.0 | 17625 | 1.0848 | 0.8917 |
| 0.0 | 48.0 | 18000 | 1.0902 | 0.8917 |
| 0.0 | 49.0 | 18375 | 1.0954 | 0.89 |
| 0.0 | 50.0 | 18750 | 1.1005 | 0.89 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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