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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_00001_fold5
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: 0.885
---
<!-- 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_00001_fold5
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: 0.9726
- Accuracy: 0.885
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2653 | 1.0 | 225 | 0.2902 | 0.8817 |
| 0.1207 | 2.0 | 450 | 0.2612 | 0.9033 |
| 0.0968 | 3.0 | 675 | 0.3296 | 0.89 |
| 0.0298 | 4.0 | 900 | 0.4559 | 0.8883 |
| 0.0117 | 5.0 | 1125 | 0.4992 | 0.875 |
| 0.0194 | 6.0 | 1350 | 0.4918 | 0.8933 |
| 0.0374 | 7.0 | 1575 | 0.6989 | 0.8833 |
| 0.0219 | 8.0 | 1800 | 0.6248 | 0.8833 |
| 0.0334 | 9.0 | 2025 | 0.5552 | 0.89 |
| 0.0279 | 10.0 | 2250 | 0.6938 | 0.8767 |
| 0.0313 | 11.0 | 2475 | 0.7037 | 0.8933 |
| 0.0467 | 12.0 | 2700 | 0.7727 | 0.8817 |
| 0.0002 | 13.0 | 2925 | 0.7310 | 0.8883 |
| 0.0003 | 14.0 | 3150 | 0.7907 | 0.8883 |
| 0.0042 | 15.0 | 3375 | 0.6719 | 0.8917 |
| 0.0001 | 16.0 | 3600 | 0.7166 | 0.8883 |
| 0.0 | 17.0 | 3825 | 0.7878 | 0.8817 |
| 0.0001 | 18.0 | 4050 | 0.8205 | 0.885 |
| 0.0463 | 19.0 | 4275 | 0.7870 | 0.9017 |
| 0.0071 | 20.0 | 4500 | 0.8873 | 0.8867 |
| 0.0 | 21.0 | 4725 | 0.8762 | 0.89 |
| 0.0 | 22.0 | 4950 | 0.8528 | 0.89 |
| 0.0 | 23.0 | 5175 | 0.8386 | 0.8867 |
| 0.0046 | 24.0 | 5400 | 0.8618 | 0.8867 |
| 0.0 | 25.0 | 5625 | 0.8575 | 0.8883 |
| 0.0 | 26.0 | 5850 | 0.8716 | 0.8883 |
| 0.0 | 27.0 | 6075 | 0.9079 | 0.8833 |
| 0.0 | 28.0 | 6300 | 0.8533 | 0.895 |
| 0.0036 | 29.0 | 6525 | 0.9107 | 0.8883 |
| 0.0 | 30.0 | 6750 | 0.9085 | 0.8917 |
| 0.0 | 31.0 | 6975 | 0.9134 | 0.8883 |
| 0.0 | 32.0 | 7200 | 0.9080 | 0.8933 |
| 0.0034 | 33.0 | 7425 | 0.9660 | 0.8883 |
| 0.0 | 34.0 | 7650 | 0.9464 | 0.8883 |
| 0.0031 | 35.0 | 7875 | 0.9525 | 0.89 |
| 0.0 | 36.0 | 8100 | 0.9223 | 0.8917 |
| 0.0032 | 37.0 | 8325 | 0.9566 | 0.89 |
| 0.0 | 38.0 | 8550 | 0.9486 | 0.8933 |
| 0.0 | 39.0 | 8775 | 0.9542 | 0.8917 |
| 0.0 | 40.0 | 9000 | 0.9546 | 0.8883 |
| 0.0 | 41.0 | 9225 | 0.9671 | 0.8867 |
| 0.0 | 42.0 | 9450 | 0.9604 | 0.885 |
| 0.0026 | 43.0 | 9675 | 0.9661 | 0.8867 |
| 0.0 | 44.0 | 9900 | 0.9673 | 0.8883 |
| 0.0 | 45.0 | 10125 | 0.9685 | 0.8883 |
| 0.0 | 46.0 | 10350 | 0.9692 | 0.8867 |
| 0.0 | 47.0 | 10575 | 0.9716 | 0.8867 |
| 0.0 | 48.0 | 10800 | 0.9722 | 0.8867 |
| 0.0 | 49.0 | 11025 | 0.9726 | 0.8867 |
| 0.0 | 50.0 | 11250 | 0.9726 | 0.885 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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