---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bridalMakeupClassifier_binary
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 1.0
    - name: Precision
      type: precision
      value: 1.0
    - name: Recall
      type: recall
      value: 1.0
    - name: F1
      type: f1
      value: 1.0
---

<!-- 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. -->

# bridalMakeupClassifier_binary

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0072
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- F1: 1.0

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2966        | 1.0   | 23   | 0.1290          | 0.9662   | 0.9432    | 0.9326 | 0.9379 |
| 0.1233        | 2.0   | 46   | 0.0407          | 0.9877   | 0.9670    | 0.9888 | 0.9778 |
| 0.0469        | 3.0   | 69   | 0.0594          | 0.9815   | 0.9368    | 1.0    | 0.9674 |
| 0.0394        | 4.0   | 92   | 0.0557          | 0.9877   | 0.9670    | 0.9888 | 0.9778 |
| 0.0909        | 5.0   | 115  | 0.0401          | 0.9908   | 0.9674    | 1.0    | 0.9834 |
| 0.05          | 6.0   | 138  | 0.0252          | 0.9877   | 0.9670    | 0.9888 | 0.9778 |
| 0.0451        | 7.0   | 161  | 0.0279          | 0.9877   | 0.9885    | 0.9663 | 0.9773 |
| 0.0231        | 8.0   | 184  | 0.0278          | 0.9938   | 0.9780    | 1.0    | 0.9889 |
| 0.0404        | 9.0   | 207  | 0.0256          | 0.9877   | 0.9775    | 0.9775 | 0.9775 |
| 0.0297        | 10.0  | 230  | 0.0260          | 0.9908   | 0.9778    | 0.9888 | 0.9832 |
| 0.0327        | 11.0  | 253  | 0.0230          | 0.9938   | 0.9780    | 1.0    | 0.9889 |
| 0.0221        | 12.0  | 276  | 0.0140          | 0.9969   | 0.9889    | 1.0    | 0.9944 |
| 0.0294        | 13.0  | 299  | 0.0106          | 0.9969   | 0.9889    | 1.0    | 0.9944 |
| 0.0292        | 14.0  | 322  | 0.0132          | 0.9969   | 0.9889    | 1.0    | 0.9944 |
| 0.0064        | 15.0  | 345  | 0.0231          | 0.9908   | 0.9674    | 1.0    | 0.9834 |
| 0.02          | 16.0  | 368  | 0.0087          | 0.9969   | 0.9889    | 1.0    | 0.9944 |
| 0.0356        | 17.0  | 391  | 0.0114          | 0.9969   | 0.9889    | 1.0    | 0.9944 |
| 0.0232        | 18.0  | 414  | 0.0072          | 1.0      | 1.0       | 1.0    | 1.0    |
| 0.0351        | 19.0  | 437  | 0.0087          | 0.9969   | 0.9889    | 1.0    | 0.9944 |
| 0.0155        | 20.0  | 460  | 0.0075          | 0.9969   | 0.9889    | 1.0    | 0.9944 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0