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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_1x_deit_tiny_rms_0001_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: 0.5813953488372093
---
<!-- 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_1x_deit_tiny_rms_0001_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: 2.2114
- Accuracy: 0.5814
## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4590 | 0.2558 |
| 2.1915 | 2.0 | 12 | 1.4820 | 0.2558 |
| 2.1915 | 3.0 | 18 | 1.4635 | 0.3488 |
| 1.4733 | 4.0 | 24 | 1.6507 | 0.2558 |
| 1.4003 | 5.0 | 30 | 1.5038 | 0.2558 |
| 1.4003 | 6.0 | 36 | 1.5372 | 0.2093 |
| 1.28 | 7.0 | 42 | 1.4420 | 0.3023 |
| 1.28 | 8.0 | 48 | 1.3681 | 0.3488 |
| 1.2064 | 9.0 | 54 | 1.4133 | 0.3023 |
| 1.1588 | 10.0 | 60 | 1.2991 | 0.4419 |
| 1.1588 | 11.0 | 66 | 1.2547 | 0.4651 |
| 1.133 | 12.0 | 72 | 1.2924 | 0.4884 |
| 1.133 | 13.0 | 78 | 1.2566 | 0.4884 |
| 1.0357 | 14.0 | 84 | 1.1915 | 0.5349 |
| 0.8616 | 15.0 | 90 | 1.2058 | 0.5116 |
| 0.8616 | 16.0 | 96 | 1.1399 | 0.5349 |
| 0.6595 | 17.0 | 102 | 1.1462 | 0.5581 |
| 0.6595 | 18.0 | 108 | 1.2856 | 0.5116 |
| 0.501 | 19.0 | 114 | 1.1528 | 0.6047 |
| 0.3761 | 20.0 | 120 | 1.2487 | 0.6047 |
| 0.3761 | 21.0 | 126 | 1.9335 | 0.5581 |
| 0.1818 | 22.0 | 132 | 2.0855 | 0.5349 |
| 0.1818 | 23.0 | 138 | 2.8198 | 0.5349 |
| 0.0677 | 24.0 | 144 | 1.5837 | 0.6279 |
| 0.0703 | 25.0 | 150 | 2.1739 | 0.5116 |
| 0.0703 | 26.0 | 156 | 2.0640 | 0.5581 |
| 0.0053 | 27.0 | 162 | 2.0886 | 0.5814 |
| 0.0053 | 28.0 | 168 | 2.1352 | 0.5814 |
| 0.0006 | 29.0 | 174 | 2.1434 | 0.5814 |
| 0.0004 | 30.0 | 180 | 2.1524 | 0.5814 |
| 0.0004 | 31.0 | 186 | 2.1594 | 0.5814 |
| 0.0003 | 32.0 | 192 | 2.1659 | 0.5814 |
| 0.0003 | 33.0 | 198 | 2.1759 | 0.5814 |
| 0.0003 | 34.0 | 204 | 2.1825 | 0.5814 |
| 0.0003 | 35.0 | 210 | 2.1918 | 0.5814 |
| 0.0003 | 36.0 | 216 | 2.1964 | 0.5814 |
| 0.0002 | 37.0 | 222 | 2.2014 | 0.5814 |
| 0.0002 | 38.0 | 228 | 2.2049 | 0.5814 |
| 0.0002 | 39.0 | 234 | 2.2075 | 0.5814 |
| 0.0002 | 40.0 | 240 | 2.2099 | 0.5814 |
| 0.0002 | 41.0 | 246 | 2.2110 | 0.5814 |
| 0.0002 | 42.0 | 252 | 2.2114 | 0.5814 |
| 0.0002 | 43.0 | 258 | 2.2114 | 0.5814 |
| 0.0002 | 44.0 | 264 | 2.2114 | 0.5814 |
| 0.0002 | 45.0 | 270 | 2.2114 | 0.5814 |
| 0.0002 | 46.0 | 276 | 2.2114 | 0.5814 |
| 0.0002 | 47.0 | 282 | 2.2114 | 0.5814 |
| 0.0002 | 48.0 | 288 | 2.2114 | 0.5814 |
| 0.0002 | 49.0 | 294 | 2.2114 | 0.5814 |
| 0.0002 | 50.0 | 300 | 2.2114 | 0.5814 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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