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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_00001_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: 0.8933333333333333
---
<!-- 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_00001_fold3
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: 0.6352
- Accuracy: 0.8933
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7543 | 1.0 | 75 | 0.6805 | 0.7367 |
| 0.4751 | 2.0 | 150 | 0.5095 | 0.8067 |
| 0.4299 | 3.0 | 225 | 0.4326 | 0.83 |
| 0.3631 | 4.0 | 300 | 0.4020 | 0.85 |
| 0.2821 | 5.0 | 375 | 0.3807 | 0.8517 |
| 0.249 | 6.0 | 450 | 0.3472 | 0.8733 |
| 0.2583 | 7.0 | 525 | 0.3378 | 0.8767 |
| 0.1796 | 8.0 | 600 | 0.3350 | 0.88 |
| 0.19 | 9.0 | 675 | 0.3308 | 0.88 |
| 0.1195 | 10.0 | 750 | 0.3410 | 0.8717 |
| 0.1538 | 11.0 | 825 | 0.3347 | 0.8767 |
| 0.1042 | 12.0 | 900 | 0.3292 | 0.895 |
| 0.1212 | 13.0 | 975 | 0.3308 | 0.895 |
| 0.0747 | 14.0 | 1050 | 0.3402 | 0.885 |
| 0.0423 | 15.0 | 1125 | 0.3519 | 0.89 |
| 0.0318 | 16.0 | 1200 | 0.3697 | 0.8867 |
| 0.0388 | 17.0 | 1275 | 0.3821 | 0.8883 |
| 0.0223 | 18.0 | 1350 | 0.3957 | 0.885 |
| 0.0206 | 19.0 | 1425 | 0.4157 | 0.885 |
| 0.0272 | 20.0 | 1500 | 0.4298 | 0.8883 |
| 0.007 | 21.0 | 1575 | 0.4227 | 0.8917 |
| 0.0064 | 22.0 | 1650 | 0.4518 | 0.895 |
| 0.0167 | 23.0 | 1725 | 0.4704 | 0.89 |
| 0.0018 | 24.0 | 1800 | 0.4600 | 0.8867 |
| 0.0144 | 25.0 | 1875 | 0.4875 | 0.89 |
| 0.0149 | 26.0 | 1950 | 0.5302 | 0.8817 |
| 0.0149 | 27.0 | 2025 | 0.5247 | 0.8917 |
| 0.001 | 28.0 | 2100 | 0.5348 | 0.8883 |
| 0.0008 | 29.0 | 2175 | 0.5323 | 0.8883 |
| 0.0008 | 30.0 | 2250 | 0.5459 | 0.89 |
| 0.0005 | 31.0 | 2325 | 0.5595 | 0.8883 |
| 0.0008 | 32.0 | 2400 | 0.5625 | 0.8917 |
| 0.0049 | 33.0 | 2475 | 0.5790 | 0.8867 |
| 0.0102 | 34.0 | 2550 | 0.5778 | 0.89 |
| 0.0263 | 35.0 | 2625 | 0.6019 | 0.89 |
| 0.01 | 36.0 | 2700 | 0.5907 | 0.8883 |
| 0.0005 | 37.0 | 2775 | 0.6086 | 0.8867 |
| 0.0003 | 38.0 | 2850 | 0.6091 | 0.8917 |
| 0.0002 | 39.0 | 2925 | 0.6105 | 0.8883 |
| 0.0002 | 40.0 | 3000 | 0.6065 | 0.8933 |
| 0.0002 | 41.0 | 3075 | 0.6175 | 0.8883 |
| 0.0165 | 42.0 | 3150 | 0.6281 | 0.8917 |
| 0.0088 | 43.0 | 3225 | 0.6246 | 0.8883 |
| 0.0003 | 44.0 | 3300 | 0.6288 | 0.89 |
| 0.0015 | 45.0 | 3375 | 0.6290 | 0.89 |
| 0.0021 | 46.0 | 3450 | 0.6320 | 0.89 |
| 0.0189 | 47.0 | 3525 | 0.6360 | 0.8867 |
| 0.0002 | 48.0 | 3600 | 0.6334 | 0.8933 |
| 0.0003 | 49.0 | 3675 | 0.6347 | 0.8933 |
| 0.0086 | 50.0 | 3750 | 0.6352 | 0.8933 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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