---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: FFPP-Raw_1FPS_faces-expand-0-aligned
  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: 0.99837772836593
    - name: Recall
      type: recall
      value: 0.993161411568177
    - name: Precision
      type: precision
      value: 0.9993696485790828
    - name: F1
      type: f1
      value: 0.9962558584033724
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# FFPP-Raw_1FPS_faces-expand-0-aligned

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: 0.0031
- Accuracy: 0.9984
- Recall: 0.9932
- Precision: 0.9994
- F1: 0.9963
- Roc Auc: 1.0000

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 | Recall | Precision | F1     | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.0983        | 1.0   | 1377  | 0.0679          | 0.9743   | 0.9700 | 0.9165    | 0.9425 | 0.9961  |
| 0.0917        | 2.0   | 2755  | 0.0342          | 0.9896   | 0.9718 | 0.9803    | 0.9760 | 0.9993  |
| 0.0291        | 3.0   | 4132  | 0.0161          | 0.9940   | 0.9908 | 0.9818    | 0.9863 | 0.9998  |
| 0.0454        | 4.0   | 5510  | 0.0136          | 0.9950   | 0.9851 | 0.9917    | 0.9884 | 0.9998  |
| 0.0302        | 5.0   | 6887  | 0.0075          | 0.9972   | 0.9896 | 0.9976    | 0.9936 | 1.0000  |
| 0.0073        | 6.0   | 8265  | 0.0064          | 0.9976   | 0.9931 | 0.9957    | 0.9944 | 1.0000  |
| 0.016         | 7.0   | 9642  | 0.0067          | 0.9975   | 0.9934 | 0.9949    | 0.9941 | 1.0000  |
| 0.0054        | 8.0   | 11020 | 0.0058          | 0.9978   | 0.9915 | 0.9984    | 0.9949 | 1.0000  |
| 0.0237        | 9.0   | 12397 | 0.0063          | 0.9975   | 0.9894 | 0.9993    | 0.9943 | 1.0000  |
| 0.0088        | 10.0  | 13775 | 0.0042          | 0.9982   | 0.9920 | 0.9995    | 0.9957 | 1.0000  |
| 0.0078        | 11.0  | 15152 | 0.0043          | 0.9982   | 0.9921 | 0.9994    | 0.9957 | 1.0000  |
| 0.0142        | 12.0  | 16530 | 0.0040          | 0.9982   | 0.9939 | 0.9979    | 0.9959 | 1.0000  |
| 0.0058        | 13.0  | 17907 | 0.0035          | 0.9983   | 0.9930 | 0.9992    | 0.9961 | 1.0000  |
| 0.0076        | 14.0  | 19285 | 0.0040          | 0.9981   | 0.9920 | 0.9994    | 0.9957 | 1.0000  |
| 0.0032        | 15.0  | 20662 | 0.0036          | 0.9983   | 0.9926 | 0.9995    | 0.9960 | 1.0000  |
| 0.0154        | 16.0  | 22040 | 0.0033          | 0.9983   | 0.9928 | 0.9996    | 0.9962 | 1.0000  |
| 0.0041        | 17.0  | 23417 | 0.0032          | 0.9984   | 0.9925 | 0.9999    | 0.9962 | 1.0000  |
| 0.002         | 18.0  | 24795 | 0.0032          | 0.9984   | 0.9933 | 0.9992    | 0.9962 | 1.0000  |
| 0.0024        | 19.0  | 26172 | 0.0031          | 0.9984   | 0.9932 | 0.9994    | 0.9963 | 1.0000  |
| 0.0023        | 19.99 | 27540 | 0.0031          | 0.9984   | 0.9927 | 0.9998    | 0.9963 | 1.0000  |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2