---
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-40-aligned_metric-acc-precision-recall-f1
  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: 0.8064887989445775
    - name: Recall
      type: recall
      value: 0.599275070479259
    - name: Precision
      type: precision
      value: 0.26912642430819317
    - name: F1
      type: f1
      value: 0.37144283574638043
---

<!-- 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. -->

# FFPP-Raw_1FPS_faces-expand-40-aligned_metric-acc-precision-recall-f1

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: 0.4464
- Accuracy: 0.8065
- Recall: 0.5993
- Precision: 0.2691
- F1: 0.3714
- Roc Auc: 0.8135

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1     | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.1821        | 1.0   | 1348 | 0.1286          | 0.9464   | 0.8533 | 0.8953    | 0.8738 | 0.9858  |
| 0.1333        | 2.0   | 2696 | 0.0715          | 0.9725   | 0.9129 | 0.9586    | 0.9352 | 0.9960  |
| 0.0809        | 3.0   | 4044 | 0.0520          | 0.9804   | 0.9344 | 0.9743    | 0.9539 | 0.9980  |


### Framework versions

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