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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_rms_00001_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: 0.8766666666666667
---
<!-- 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_rms_00001_fold4
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: 1.1238
- Accuracy: 0.8767
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2323 | 1.0 | 225 | 0.3035 | 0.8817 |
| 0.1222 | 2.0 | 450 | 0.3392 | 0.8867 |
| 0.0332 | 3.0 | 675 | 0.4670 | 0.875 |
| 0.0456 | 4.0 | 900 | 0.5479 | 0.8817 |
| 0.0021 | 5.0 | 1125 | 0.5591 | 0.8867 |
| 0.0132 | 6.0 | 1350 | 0.7507 | 0.8633 |
| 0.0101 | 7.0 | 1575 | 0.7420 | 0.8883 |
| 0.0016 | 8.0 | 1800 | 0.6836 | 0.8933 |
| 0.0523 | 9.0 | 2025 | 0.8255 | 0.8783 |
| 0.0254 | 10.0 | 2250 | 1.1197 | 0.8483 |
| 0.0014 | 11.0 | 2475 | 0.7120 | 0.885 |
| 0.0 | 12.0 | 2700 | 0.7666 | 0.8917 |
| 0.0178 | 13.0 | 2925 | 0.6967 | 0.8917 |
| 0.0002 | 14.0 | 3150 | 0.8484 | 0.8867 |
| 0.0577 | 15.0 | 3375 | 0.8550 | 0.885 |
| 0.0 | 16.0 | 3600 | 0.8425 | 0.89 |
| 0.0024 | 17.0 | 3825 | 0.8953 | 0.8767 |
| 0.0 | 18.0 | 4050 | 0.9355 | 0.885 |
| 0.0001 | 19.0 | 4275 | 0.8404 | 0.89 |
| 0.0 | 20.0 | 4500 | 0.8809 | 0.885 |
| 0.0172 | 21.0 | 4725 | 0.8605 | 0.8883 |
| 0.0 | 22.0 | 4950 | 0.9436 | 0.8817 |
| 0.0323 | 23.0 | 5175 | 0.9309 | 0.8833 |
| 0.0 | 24.0 | 5400 | 0.9068 | 0.89 |
| 0.0 | 25.0 | 5625 | 0.9079 | 0.8817 |
| 0.0 | 26.0 | 5850 | 0.9066 | 0.89 |
| 0.0 | 27.0 | 6075 | 1.0773 | 0.87 |
| 0.0 | 28.0 | 6300 | 1.1035 | 0.8717 |
| 0.0 | 29.0 | 6525 | 1.0736 | 0.8717 |
| 0.0001 | 30.0 | 6750 | 1.1428 | 0.8733 |
| 0.0 | 31.0 | 6975 | 1.0098 | 0.8767 |
| 0.0 | 32.0 | 7200 | 1.0179 | 0.88 |
| 0.0003 | 33.0 | 7425 | 1.0539 | 0.875 |
| 0.0 | 34.0 | 7650 | 1.0462 | 0.8783 |
| 0.0 | 35.0 | 7875 | 1.0532 | 0.8817 |
| 0.0 | 36.0 | 8100 | 1.0591 | 0.8783 |
| 0.0 | 37.0 | 8325 | 1.0682 | 0.8783 |
| 0.0 | 38.0 | 8550 | 1.0909 | 0.8783 |
| 0.0 | 39.0 | 8775 | 1.0760 | 0.8833 |
| 0.0 | 40.0 | 9000 | 1.0817 | 0.8733 |
| 0.0 | 41.0 | 9225 | 1.0943 | 0.8717 |
| 0.003 | 42.0 | 9450 | 1.1042 | 0.8767 |
| 0.0 | 43.0 | 9675 | 1.0995 | 0.875 |
| 0.0027 | 44.0 | 9900 | 1.1108 | 0.8767 |
| 0.0 | 45.0 | 10125 | 1.1127 | 0.8783 |
| 0.0 | 46.0 | 10350 | 1.1166 | 0.8783 |
| 0.0 | 47.0 | 10575 | 1.1195 | 0.8783 |
| 0.0 | 48.0 | 10800 | 1.1208 | 0.8783 |
| 0.0 | 49.0 | 11025 | 1.1237 | 0.8767 |
| 0.0 | 50.0 | 11250 | 1.1238 | 0.8767 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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