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---
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
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