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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_sgd_00001_fold4
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: 0.43333333333333335
---
<!-- 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_sgd_00001_fold4
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: 1.0826
- Accuracy: 0.4333
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1201 | 1.0 | 225 | 1.1112 | 0.3333 |
| 1.1056 | 2.0 | 450 | 1.1099 | 0.34 |
| 1.0987 | 3.0 | 675 | 1.1086 | 0.3433 |
| 1.1099 | 4.0 | 900 | 1.1074 | 0.355 |
| 1.0994 | 5.0 | 1125 | 1.1062 | 0.3517 |
| 1.106 | 6.0 | 1350 | 1.1051 | 0.3583 |
| 1.1031 | 7.0 | 1575 | 1.1040 | 0.3633 |
| 1.1065 | 8.0 | 1800 | 1.1029 | 0.37 |
| 1.0902 | 9.0 | 2025 | 1.1018 | 0.3683 |
| 1.0803 | 10.0 | 2250 | 1.1008 | 0.3717 |
| 1.0894 | 11.0 | 2475 | 1.0998 | 0.375 |
| 1.095 | 12.0 | 2700 | 1.0989 | 0.3817 |
| 1.0882 | 13.0 | 2925 | 1.0979 | 0.3867 |
| 1.0908 | 14.0 | 3150 | 1.0971 | 0.39 |
| 1.1022 | 15.0 | 3375 | 1.0962 | 0.3917 |
| 1.0922 | 16.0 | 3600 | 1.0954 | 0.395 |
| 1.0943 | 17.0 | 3825 | 1.0946 | 0.3967 |
| 1.0851 | 18.0 | 4050 | 1.0938 | 0.4017 |
| 1.0874 | 19.0 | 4275 | 1.0931 | 0.405 |
| 1.0966 | 20.0 | 4500 | 1.0924 | 0.4083 |
| 1.0868 | 21.0 | 4725 | 1.0917 | 0.4083 |
| 1.0765 | 22.0 | 4950 | 1.0910 | 0.4083 |
| 1.0918 | 23.0 | 5175 | 1.0904 | 0.41 |
| 1.0777 | 24.0 | 5400 | 1.0898 | 0.4183 |
| 1.0939 | 25.0 | 5625 | 1.0892 | 0.42 |
| 1.0798 | 26.0 | 5850 | 1.0886 | 0.4217 |
| 1.0858 | 27.0 | 6075 | 1.0881 | 0.425 |
| 1.061 | 28.0 | 6300 | 1.0876 | 0.4233 |
| 1.083 | 29.0 | 6525 | 1.0871 | 0.425 |
| 1.0868 | 30.0 | 6750 | 1.0867 | 0.425 |
| 1.0886 | 31.0 | 6975 | 1.0862 | 0.4267 |
| 1.0841 | 32.0 | 7200 | 1.0858 | 0.4267 |
| 1.0853 | 33.0 | 7425 | 1.0855 | 0.4283 |
| 1.0704 | 34.0 | 7650 | 1.0851 | 0.4283 |
| 1.0702 | 35.0 | 7875 | 1.0848 | 0.4267 |
| 1.0848 | 36.0 | 8100 | 1.0845 | 0.4283 |
| 1.0671 | 37.0 | 8325 | 1.0842 | 0.4283 |
| 1.0578 | 38.0 | 8550 | 1.0840 | 0.43 |
| 1.0817 | 39.0 | 8775 | 1.0837 | 0.43 |
| 1.0866 | 40.0 | 9000 | 1.0835 | 0.4317 |
| 1.083 | 41.0 | 9225 | 1.0833 | 0.4333 |
| 1.0747 | 42.0 | 9450 | 1.0832 | 0.4333 |
| 1.0816 | 43.0 | 9675 | 1.0830 | 0.4333 |
| 1.0657 | 44.0 | 9900 | 1.0829 | 0.4333 |
| 1.0619 | 45.0 | 10125 | 1.0828 | 0.4333 |
| 1.067 | 46.0 | 10350 | 1.0827 | 0.4333 |
| 1.0593 | 47.0 | 10575 | 1.0827 | 0.4333 |
| 1.0587 | 48.0 | 10800 | 1.0826 | 0.4333 |
| 1.0675 | 49.0 | 11025 | 1.0826 | 0.4333 |
| 1.0632 | 50.0 | 11250 | 1.0826 | 0.4333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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