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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_fold1
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: 0.8514190317195326
---
<!-- 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_fold1
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: 0.7515
- Accuracy: 0.8514
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6291 | 1.0 | 76 | 0.6157 | 0.7212 |
| 0.4871 | 2.0 | 152 | 0.4955 | 0.7663 |
| 0.3563 | 3.0 | 228 | 0.4334 | 0.8147 |
| 0.3532 | 4.0 | 304 | 0.4038 | 0.8264 |
| 0.2556 | 5.0 | 380 | 0.3826 | 0.8364 |
| 0.1832 | 6.0 | 456 | 0.3763 | 0.8314 |
| 0.188 | 7.0 | 532 | 0.3632 | 0.8364 |
| 0.176 | 8.0 | 608 | 0.3509 | 0.8531 |
| 0.1753 | 9.0 | 684 | 0.3927 | 0.8464 |
| 0.094 | 10.0 | 760 | 0.3701 | 0.8614 |
| 0.1113 | 11.0 | 836 | 0.3673 | 0.8598 |
| 0.0773 | 12.0 | 912 | 0.3708 | 0.8531 |
| 0.0733 | 13.0 | 988 | 0.3810 | 0.8464 |
| 0.0524 | 14.0 | 1064 | 0.3916 | 0.8648 |
| 0.0392 | 15.0 | 1140 | 0.4052 | 0.8648 |
| 0.0271 | 16.0 | 1216 | 0.4245 | 0.8614 |
| 0.0255 | 17.0 | 1292 | 0.4381 | 0.8514 |
| 0.0233 | 18.0 | 1368 | 0.4614 | 0.8698 |
| 0.0233 | 19.0 | 1444 | 0.4762 | 0.8614 |
| 0.0102 | 20.0 | 1520 | 0.4954 | 0.8664 |
| 0.0235 | 21.0 | 1596 | 0.5367 | 0.8564 |
| 0.0283 | 22.0 | 1672 | 0.5394 | 0.8681 |
| 0.0037 | 23.0 | 1748 | 0.5607 | 0.8598 |
| 0.0016 | 24.0 | 1824 | 0.5901 | 0.8564 |
| 0.0188 | 25.0 | 1900 | 0.5950 | 0.8564 |
| 0.0156 | 26.0 | 1976 | 0.6264 | 0.8531 |
| 0.0197 | 27.0 | 2052 | 0.6288 | 0.8598 |
| 0.009 | 28.0 | 2128 | 0.6474 | 0.8531 |
| 0.025 | 29.0 | 2204 | 0.6597 | 0.8564 |
| 0.0005 | 30.0 | 2280 | 0.6571 | 0.8548 |
| 0.0131 | 31.0 | 2356 | 0.6711 | 0.8531 |
| 0.0183 | 32.0 | 2432 | 0.6793 | 0.8581 |
| 0.0003 | 33.0 | 2508 | 0.6998 | 0.8514 |
| 0.0004 | 34.0 | 2584 | 0.6868 | 0.8564 |
| 0.0226 | 35.0 | 2660 | 0.7047 | 0.8564 |
| 0.0145 | 36.0 | 2736 | 0.7054 | 0.8548 |
| 0.0171 | 37.0 | 2812 | 0.7259 | 0.8464 |
| 0.0011 | 38.0 | 2888 | 0.7392 | 0.8481 |
| 0.0066 | 39.0 | 2964 | 0.7347 | 0.8481 |
| 0.0002 | 40.0 | 3040 | 0.7257 | 0.8564 |
| 0.0087 | 41.0 | 3116 | 0.7270 | 0.8548 |
| 0.0004 | 42.0 | 3192 | 0.7348 | 0.8631 |
| 0.0075 | 43.0 | 3268 | 0.7382 | 0.8564 |
| 0.0002 | 44.0 | 3344 | 0.7585 | 0.8447 |
| 0.0002 | 45.0 | 3420 | 0.7418 | 0.8531 |
| 0.0002 | 46.0 | 3496 | 0.7509 | 0.8497 |
| 0.0002 | 47.0 | 3572 | 0.7508 | 0.8514 |
| 0.0054 | 48.0 | 3648 | 0.7479 | 0.8514 |
| 0.0002 | 49.0 | 3724 | 0.7520 | 0.8514 |
| 0.0002 | 50.0 | 3800 | 0.7515 | 0.8514 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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