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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: hushem_5x_deit_tiny_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: 0.7209302325581395
---
<!-- 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. -->
# hushem_5x_deit_tiny_adamax_001_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: 1.8507
- Accuracy: 0.7209
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4597 | 1.0 | 28 | 1.3552 | 0.3256 |
| 1.2609 | 2.0 | 56 | 0.9015 | 0.6744 |
| 1.1559 | 3.0 | 84 | 2.3181 | 0.3256 |
| 1.0822 | 4.0 | 112 | 1.6592 | 0.4186 |
| 1.0553 | 5.0 | 140 | 0.9222 | 0.4651 |
| 0.9651 | 6.0 | 168 | 0.7306 | 0.7442 |
| 0.9771 | 7.0 | 196 | 0.7827 | 0.6279 |
| 0.9016 | 8.0 | 224 | 0.9874 | 0.5581 |
| 0.8187 | 9.0 | 252 | 1.0233 | 0.5349 |
| 0.8405 | 10.0 | 280 | 0.6414 | 0.7674 |
| 0.7808 | 11.0 | 308 | 0.6637 | 0.6977 |
| 0.8152 | 12.0 | 336 | 0.5374 | 0.8605 |
| 0.8044 | 13.0 | 364 | 0.5684 | 0.7442 |
| 0.7275 | 14.0 | 392 | 0.6622 | 0.8140 |
| 0.6931 | 15.0 | 420 | 0.7767 | 0.7209 |
| 0.691 | 16.0 | 448 | 0.5520 | 0.7674 |
| 0.7585 | 17.0 | 476 | 0.6770 | 0.7674 |
| 0.6303 | 18.0 | 504 | 0.6834 | 0.7442 |
| 0.6578 | 19.0 | 532 | 0.7776 | 0.6977 |
| 0.4934 | 20.0 | 560 | 1.1067 | 0.7442 |
| 0.5397 | 21.0 | 588 | 0.7250 | 0.7674 |
| 0.5586 | 22.0 | 616 | 0.9824 | 0.6047 |
| 0.4808 | 23.0 | 644 | 0.9582 | 0.7442 |
| 0.4823 | 24.0 | 672 | 0.9114 | 0.6279 |
| 0.4124 | 25.0 | 700 | 1.1614 | 0.7209 |
| 0.3991 | 26.0 | 728 | 1.3579 | 0.6279 |
| 0.5138 | 27.0 | 756 | 1.5915 | 0.6512 |
| 0.3857 | 28.0 | 784 | 0.6799 | 0.8140 |
| 0.3797 | 29.0 | 812 | 1.1771 | 0.7907 |
| 0.3781 | 30.0 | 840 | 0.9809 | 0.7674 |
| 0.3225 | 31.0 | 868 | 0.7120 | 0.8140 |
| 0.3017 | 32.0 | 896 | 0.9984 | 0.7674 |
| 0.2468 | 33.0 | 924 | 1.0271 | 0.7674 |
| 0.1651 | 34.0 | 952 | 1.2194 | 0.7674 |
| 0.254 | 35.0 | 980 | 1.2984 | 0.7907 |
| 0.1644 | 36.0 | 1008 | 1.3708 | 0.7674 |
| 0.1972 | 37.0 | 1036 | 1.6461 | 0.7209 |
| 0.1252 | 38.0 | 1064 | 1.8473 | 0.7442 |
| 0.1252 | 39.0 | 1092 | 1.5823 | 0.7209 |
| 0.4334 | 40.0 | 1120 | 1.7311 | 0.7209 |
| 0.1029 | 41.0 | 1148 | 1.7390 | 0.7674 |
| 0.0992 | 42.0 | 1176 | 1.6959 | 0.7907 |
| 0.0993 | 43.0 | 1204 | 1.9237 | 0.7442 |
| 0.0435 | 44.0 | 1232 | 1.8569 | 0.7442 |
| 0.054 | 45.0 | 1260 | 1.8007 | 0.7907 |
| 0.0678 | 46.0 | 1288 | 1.8496 | 0.7442 |
| 0.0576 | 47.0 | 1316 | 1.8145 | 0.7442 |
| 0.0232 | 48.0 | 1344 | 1.8497 | 0.7209 |
| 0.0393 | 49.0 | 1372 | 1.8507 | 0.7209 |
| 0.0213 | 50.0 | 1400 | 1.8507 | 0.7209 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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