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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_5x_deit_base_rms_0001_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: 0.9081803005008348
---
<!-- 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_5x_deit_base_rms_0001_fold1
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: 0.8795
- Accuracy: 0.9082
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.232 | 1.0 | 376 | 0.3558 | 0.8531 |
| 0.1372 | 2.0 | 752 | 0.3215 | 0.8898 |
| 0.1203 | 3.0 | 1128 | 0.3886 | 0.8881 |
| 0.0697 | 4.0 | 1504 | 0.4340 | 0.8765 |
| 0.0324 | 5.0 | 1880 | 0.5132 | 0.8865 |
| 0.0728 | 6.0 | 2256 | 0.5310 | 0.8881 |
| 0.0495 | 7.0 | 2632 | 0.6186 | 0.8781 |
| 0.0797 | 8.0 | 3008 | 0.5758 | 0.8881 |
| 0.0121 | 9.0 | 3384 | 0.5420 | 0.8998 |
| 0.0399 | 10.0 | 3760 | 0.6888 | 0.8815 |
| 0.0293 | 11.0 | 4136 | 0.5816 | 0.9065 |
| 0.0382 | 12.0 | 4512 | 0.6430 | 0.8781 |
| 0.0148 | 13.0 | 4888 | 0.8464 | 0.8781 |
| 0.0249 | 14.0 | 5264 | 0.5972 | 0.8848 |
| 0.0008 | 15.0 | 5640 | 0.6413 | 0.8965 |
| 0.0409 | 16.0 | 6016 | 0.7158 | 0.8798 |
| 0.0202 | 17.0 | 6392 | 0.7167 | 0.8815 |
| 0.0253 | 18.0 | 6768 | 0.6005 | 0.8998 |
| 0.0002 | 19.0 | 7144 | 0.6775 | 0.8948 |
| 0.0275 | 20.0 | 7520 | 0.7685 | 0.8948 |
| 0.0272 | 21.0 | 7896 | 0.6847 | 0.8965 |
| 0.0003 | 22.0 | 8272 | 0.7613 | 0.8915 |
| 0.0008 | 23.0 | 8648 | 0.7342 | 0.8865 |
| 0.0001 | 24.0 | 9024 | 0.7590 | 0.8698 |
| 0.0 | 25.0 | 9400 | 0.7885 | 0.8898 |
| 0.0032 | 26.0 | 9776 | 0.6867 | 0.8982 |
| 0.0 | 27.0 | 10152 | 0.7507 | 0.8948 |
| 0.0003 | 28.0 | 10528 | 0.7142 | 0.8848 |
| 0.0043 | 29.0 | 10904 | 0.6904 | 0.8881 |
| 0.0066 | 30.0 | 11280 | 0.7736 | 0.8898 |
| 0.0007 | 31.0 | 11656 | 0.7358 | 0.8998 |
| 0.0006 | 32.0 | 12032 | 1.0875 | 0.8598 |
| 0.0 | 33.0 | 12408 | 0.7340 | 0.9015 |
| 0.0 | 34.0 | 12784 | 0.7139 | 0.8982 |
| 0.0 | 35.0 | 13160 | 0.7525 | 0.9115 |
| 0.0002 | 36.0 | 13536 | 0.7504 | 0.8982 |
| 0.0 | 37.0 | 13912 | 0.8006 | 0.8982 |
| 0.0 | 38.0 | 14288 | 0.7615 | 0.9015 |
| 0.0 | 39.0 | 14664 | 0.7609 | 0.9115 |
| 0.0 | 40.0 | 15040 | 0.8059 | 0.9015 |
| 0.0 | 41.0 | 15416 | 0.8037 | 0.9032 |
| 0.0 | 42.0 | 15792 | 0.8697 | 0.9048 |
| 0.0 | 43.0 | 16168 | 0.8414 | 0.9115 |
| 0.0 | 44.0 | 16544 | 0.8687 | 0.9098 |
| 0.0 | 45.0 | 16920 | 0.8833 | 0.9065 |
| 0.0031 | 46.0 | 17296 | 0.8963 | 0.9065 |
| 0.0 | 47.0 | 17672 | 0.8765 | 0.9082 |
| 0.0 | 48.0 | 18048 | 0.8724 | 0.9082 |
| 0.0 | 49.0 | 18424 | 0.8783 | 0.9082 |
| 0.0025 | 50.0 | 18800 | 0.8795 | 0.9082 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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