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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_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: 0.76
---
<!-- 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_001_fold3
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: 0.5835
- Accuracy: 0.76
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9577 | 1.0 | 225 | 0.9911 | 0.4833 |
| 0.8945 | 2.0 | 450 | 0.8723 | 0.515 |
| 0.9126 | 3.0 | 675 | 0.9098 | 0.5583 |
| 0.864 | 4.0 | 900 | 0.9590 | 0.4883 |
| 0.7818 | 5.0 | 1125 | 0.8052 | 0.61 |
| 0.8173 | 6.0 | 1350 | 0.8349 | 0.57 |
| 0.8234 | 7.0 | 1575 | 0.8296 | 0.5633 |
| 0.8025 | 8.0 | 1800 | 0.7926 | 0.6433 |
| 0.794 | 9.0 | 2025 | 0.7671 | 0.6367 |
| 0.7364 | 10.0 | 2250 | 0.7623 | 0.6917 |
| 0.7539 | 11.0 | 2475 | 0.7462 | 0.6567 |
| 0.7493 | 12.0 | 2700 | 0.7599 | 0.6483 |
| 0.8066 | 13.0 | 2925 | 0.7984 | 0.6233 |
| 0.7644 | 14.0 | 3150 | 0.7284 | 0.6767 |
| 0.6406 | 15.0 | 3375 | 0.8986 | 0.6117 |
| 0.7539 | 16.0 | 3600 | 0.7380 | 0.6417 |
| 0.7079 | 17.0 | 3825 | 0.7519 | 0.6483 |
| 0.7112 | 18.0 | 4050 | 0.7274 | 0.6683 |
| 0.709 | 19.0 | 4275 | 0.7182 | 0.6783 |
| 0.6627 | 20.0 | 4500 | 0.6933 | 0.6917 |
| 0.62 | 21.0 | 4725 | 0.7192 | 0.6783 |
| 0.6351 | 22.0 | 4950 | 0.6854 | 0.6967 |
| 0.6169 | 23.0 | 5175 | 0.6958 | 0.6917 |
| 0.6173 | 24.0 | 5400 | 0.6916 | 0.6867 |
| 0.6807 | 25.0 | 5625 | 0.6783 | 0.705 |
| 0.6099 | 26.0 | 5850 | 0.6681 | 0.705 |
| 0.5604 | 27.0 | 6075 | 0.7149 | 0.6767 |
| 0.6004 | 28.0 | 6300 | 0.7253 | 0.6667 |
| 0.6392 | 29.0 | 6525 | 0.6891 | 0.66 |
| 0.5659 | 30.0 | 6750 | 0.6273 | 0.7267 |
| 0.5546 | 31.0 | 6975 | 0.6350 | 0.7317 |
| 0.5835 | 32.0 | 7200 | 0.6529 | 0.6983 |
| 0.6237 | 33.0 | 7425 | 0.6048 | 0.7233 |
| 0.5674 | 34.0 | 7650 | 0.6396 | 0.7167 |
| 0.5405 | 35.0 | 7875 | 0.6074 | 0.7183 |
| 0.5745 | 36.0 | 8100 | 0.5947 | 0.7317 |
| 0.5811 | 37.0 | 8325 | 0.5820 | 0.7383 |
| 0.5642 | 38.0 | 8550 | 0.5685 | 0.7433 |
| 0.5332 | 39.0 | 8775 | 0.5891 | 0.745 |
| 0.5278 | 40.0 | 9000 | 0.5919 | 0.7283 |
| 0.5007 | 41.0 | 9225 | 0.5742 | 0.7567 |
| 0.5377 | 42.0 | 9450 | 0.5885 | 0.76 |
| 0.4913 | 43.0 | 9675 | 0.5649 | 0.755 |
| 0.5315 | 44.0 | 9900 | 0.5703 | 0.74 |
| 0.4857 | 45.0 | 10125 | 0.5619 | 0.765 |
| 0.4747 | 46.0 | 10350 | 0.5832 | 0.7533 |
| 0.5553 | 47.0 | 10575 | 0.5734 | 0.755 |
| 0.452 | 48.0 | 10800 | 0.5866 | 0.7617 |
| 0.4761 | 49.0 | 11025 | 0.5792 | 0.7567 |
| 0.4755 | 50.0 | 11250 | 0.5835 | 0.76 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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