---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
model-index:
- name: finalProject
  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: 0.9890023566378633
    - name: Precision
      type: precision
      value: 0.9894345375382527
---

<!-- 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. -->

# finalProject

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0411
- Accuracy: 0.9890
- F1 Score: 0.9892
- Precision: 0.9894
- Sensitivity: 0.9891
- Specificity: 0.9972

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 0.3384        | 1.0   | 30   | 0.2387          | 0.9144   | 0.9163   | 0.9197    | 0.9146      | 0.9781      |
| 0.1608        | 2.0   | 60   | 0.1635          | 0.9466   | 0.9476   | 0.9485    | 0.9474      | 0.9865      |
| 0.0953        | 3.0   | 90   | 0.0915          | 0.9698   | 0.9703   | 0.9706    | 0.9706      | 0.9924      |
| 0.0573        | 4.0   | 120  | 0.1125          | 0.9607   | 0.9617   | 0.9634    | 0.9621      | 0.9901      |
| 0.0335        | 5.0   | 150  | 0.0536          | 0.9827   | 0.9831   | 0.9837    | 0.9826      | 0.9957      |
| 0.0185        | 6.0   | 180  | 0.0543          | 0.9827   | 0.9830   | 0.9837    | 0.9825      | 0.9957      |
| 0.0226        | 7.0   | 210  | 0.0478          | 0.9859   | 0.9861   | 0.9866    | 0.9856      | 0.9965      |
| 0.0131        | 8.0   | 240  | 0.0468          | 0.9843   | 0.9846   | 0.9847    | 0.9846      | 0.9961      |
| 0.0087        | 9.0   | 270  | 0.0411          | 0.9890   | 0.9892   | 0.9894    | 0.9891      | 0.9972      |
| 0.0043        | 10.0  | 300  | 0.0376          | 0.9886   | 0.9888   | 0.9890    | 0.9887      | 0.9971      |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3