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---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_small_rms_0001_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: 0.8933333333333333
---
<!-- 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_small_rms_0001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0792
- Accuracy: 0.8933
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.279 | 1.0 | 375 | 0.3229 | 0.88 |
| 0.164 | 2.0 | 750 | 0.3138 | 0.91 |
| 0.1109 | 3.0 | 1125 | 0.3763 | 0.8967 |
| 0.0429 | 4.0 | 1500 | 0.4357 | 0.8933 |
| 0.0647 | 5.0 | 1875 | 0.5383 | 0.9 |
| 0.0292 | 6.0 | 2250 | 0.4950 | 0.8983 |
| 0.0793 | 7.0 | 2625 | 0.5600 | 0.8867 |
| 0.0326 | 8.0 | 3000 | 0.6289 | 0.885 |
| 0.0175 | 9.0 | 3375 | 0.6125 | 0.89 |
| 0.0162 | 10.0 | 3750 | 0.7037 | 0.8983 |
| 0.0357 | 11.0 | 4125 | 0.6928 | 0.8833 |
| 0.0295 | 12.0 | 4500 | 0.7344 | 0.8817 |
| 0.0007 | 13.0 | 4875 | 0.6848 | 0.9033 |
| 0.0001 | 14.0 | 5250 | 0.6912 | 0.89 |
| 0.0101 | 15.0 | 5625 | 0.6507 | 0.9 |
| 0.024 | 16.0 | 6000 | 0.6949 | 0.8933 |
| 0.0004 | 17.0 | 6375 | 0.5735 | 0.9133 |
| 0.0116 | 18.0 | 6750 | 0.6520 | 0.905 |
| 0.0214 | 19.0 | 7125 | 0.8822 | 0.895 |
| 0.0002 | 20.0 | 7500 | 0.7795 | 0.8883 |
| 0.023 | 21.0 | 7875 | 0.7295 | 0.9017 |
| 0.0001 | 22.0 | 8250 | 0.7805 | 0.9 |
| 0.0077 | 23.0 | 8625 | 0.7822 | 0.89 |
| 0.0248 | 24.0 | 9000 | 0.6997 | 0.9017 |
| 0.0192 | 25.0 | 9375 | 0.7612 | 0.8983 |
| 0.0053 | 26.0 | 9750 | 0.6937 | 0.9 |
| 0.0001 | 27.0 | 10125 | 0.8197 | 0.9 |
| 0.0066 | 28.0 | 10500 | 0.7264 | 0.9033 |
| 0.0 | 29.0 | 10875 | 0.9769 | 0.8883 |
| 0.0055 | 30.0 | 11250 | 0.9308 | 0.8817 |
| 0.0083 | 31.0 | 11625 | 0.9275 | 0.88 |
| 0.0 | 32.0 | 12000 | 0.8761 | 0.8917 |
| 0.0001 | 33.0 | 12375 | 0.8754 | 0.8967 |
| 0.0001 | 34.0 | 12750 | 0.9281 | 0.8883 |
| 0.0158 | 35.0 | 13125 | 1.0369 | 0.8767 |
| 0.0043 | 36.0 | 13500 | 1.0161 | 0.88 |
| 0.0023 | 37.0 | 13875 | 0.9274 | 0.8933 |
| 0.0 | 38.0 | 14250 | 0.9705 | 0.8933 |
| 0.0 | 39.0 | 14625 | 1.0691 | 0.8867 |
| 0.0029 | 40.0 | 15000 | 1.0780 | 0.89 |
| 0.0 | 41.0 | 15375 | 1.0592 | 0.885 |
| 0.0 | 42.0 | 15750 | 1.0784 | 0.885 |
| 0.0 | 43.0 | 16125 | 1.0389 | 0.8917 |
| 0.0 | 44.0 | 16500 | 1.0434 | 0.8933 |
| 0.0 | 45.0 | 16875 | 1.0581 | 0.8917 |
| 0.0 | 46.0 | 17250 | 1.0632 | 0.8933 |
| 0.0 | 47.0 | 17625 | 1.0692 | 0.8933 |
| 0.0 | 48.0 | 18000 | 1.0755 | 0.8917 |
| 0.0 | 49.0 | 18375 | 1.0789 | 0.8917 |
| 0.0 | 50.0 | 18750 | 1.0792 | 0.8933 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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