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---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_tiny_rms_00001_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: 0.8831385642737897
---
<!-- 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_tiny_rms_00001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co./facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9365
- Accuracy: 0.8831
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3997 | 1.0 | 226 | 0.3347 | 0.8631 |
| 0.255 | 2.0 | 452 | 0.2918 | 0.8831 |
| 0.1424 | 3.0 | 678 | 0.3193 | 0.8748 |
| 0.1606 | 4.0 | 904 | 0.3236 | 0.8865 |
| 0.1532 | 5.0 | 1130 | 0.3062 | 0.8915 |
| 0.0588 | 6.0 | 1356 | 0.4276 | 0.8915 |
| 0.0136 | 7.0 | 1582 | 0.4629 | 0.8831 |
| 0.0352 | 8.0 | 1808 | 0.5602 | 0.8765 |
| 0.0656 | 9.0 | 2034 | 0.5379 | 0.8765 |
| 0.0146 | 10.0 | 2260 | 0.6661 | 0.8881 |
| 0.0002 | 11.0 | 2486 | 0.7507 | 0.8798 |
| 0.015 | 12.0 | 2712 | 0.6981 | 0.8865 |
| 0.0138 | 13.0 | 2938 | 0.9249 | 0.8715 |
| 0.0124 | 14.0 | 3164 | 0.8454 | 0.8748 |
| 0.0002 | 15.0 | 3390 | 0.8233 | 0.8781 |
| 0.0006 | 16.0 | 3616 | 0.8574 | 0.8698 |
| 0.0171 | 17.0 | 3842 | 0.8765 | 0.8781 |
| 0.0 | 18.0 | 4068 | 0.8826 | 0.8865 |
| 0.0173 | 19.0 | 4294 | 0.7556 | 0.8932 |
| 0.0158 | 20.0 | 4520 | 0.9424 | 0.8748 |
| 0.0001 | 21.0 | 4746 | 1.0298 | 0.8648 |
| 0.0133 | 22.0 | 4972 | 0.9420 | 0.8664 |
| 0.0145 | 23.0 | 5198 | 0.8626 | 0.8865 |
| 0.0001 | 24.0 | 5424 | 0.9250 | 0.8781 |
| 0.0 | 25.0 | 5650 | 0.8112 | 0.8948 |
| 0.0002 | 26.0 | 5876 | 0.8569 | 0.8898 |
| 0.0 | 27.0 | 6102 | 0.8070 | 0.8915 |
| 0.0 | 28.0 | 6328 | 0.8507 | 0.8765 |
| 0.0 | 29.0 | 6554 | 0.8437 | 0.8932 |
| 0.0 | 30.0 | 6780 | 0.8816 | 0.8848 |
| 0.0 | 31.0 | 7006 | 0.8733 | 0.8848 |
| 0.0 | 32.0 | 7232 | 0.9948 | 0.8681 |
| 0.0082 | 33.0 | 7458 | 0.9148 | 0.8831 |
| 0.0 | 34.0 | 7684 | 0.9131 | 0.8881 |
| 0.0 | 35.0 | 7910 | 0.9403 | 0.8781 |
| 0.0 | 36.0 | 8136 | 0.9014 | 0.8765 |
| 0.0 | 37.0 | 8362 | 0.9056 | 0.8798 |
| 0.0 | 38.0 | 8588 | 0.9375 | 0.8781 |
| 0.0 | 39.0 | 8814 | 0.9025 | 0.8831 |
| 0.0 | 40.0 | 9040 | 0.9205 | 0.8815 |
| 0.0051 | 41.0 | 9266 | 0.9089 | 0.8848 |
| 0.0025 | 42.0 | 9492 | 0.9223 | 0.8848 |
| 0.0 | 43.0 | 9718 | 0.9136 | 0.8881 |
| 0.0 | 44.0 | 9944 | 0.9207 | 0.8848 |
| 0.0 | 45.0 | 10170 | 0.9266 | 0.8831 |
| 0.0 | 46.0 | 10396 | 0.9325 | 0.8865 |
| 0.0 | 47.0 | 10622 | 0.9382 | 0.8815 |
| 0.0 | 48.0 | 10848 | 0.9372 | 0.8815 |
| 0.0 | 49.0 | 11074 | 0.9372 | 0.8831 |
| 0.0 | 50.0 | 11300 | 0.9365 | 0.8831 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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