---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
model-index:
- name: model_0-D2-SW-Tuned
  results: []
---

<!-- 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. -->

# model_0-D2-SW-Tuned

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1783
- Accuracy: 0.9889
- Precision: 0.9889
- Sensitivity: 0.9667
- Specificity: 0.9933
- F1: 0.9889
- Auc: 0.9991
- Mcc: 0.96
- J Stat: 0.96
- Confusion Matrix: [[149, 1], [1, 29]]

## 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: 4.286661471431606e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.07159086635599052
- num_epochs: 7
- label_smoothing_factor: 0.0713211794743841
- weight_decay: 0.010753387469303224

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Sensitivity | Specificity | F1     | Auc    | Mcc    | J Stat | Confusion Matrix    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:-----------:|:-----------:|:------:|:------:|:------:|:------:|:-------------------:|
| 0.2816        | 1.0   | 376  | 0.2185          | 0.9722   | 0.9727    | 0.9333      | 0.98        | 0.9724 | 0.9929 | 0.9015 | 0.9133 | [[147, 3], [2, 28]] |
| 0.2998        | 2.0   | 752  | 0.2313          | 0.9611   | 0.9607    | 0.8667      | 0.98        | 0.9608 | 0.9947 | 0.8583 | 0.8467 | [[147, 3], [4, 26]] |
| 0.1991        | 3.0   | 1128 | 0.2396          | 0.9667   | 0.9662    | 0.8667      | 0.9867      | 0.9662 | 0.9951 | 0.8775 | 0.8533 | [[148, 2], [4, 26]] |
| 0.2611        | 4.0   | 1504 | 0.1923          | 0.9833   | 0.9837    | 0.9667      | 0.9867      | 0.9834 | 0.998  | 0.9410 | 0.9533 | [[148, 2], [1, 29]] |
| 0.2044        | 5.0   | 1880 | 0.2105          | 0.9722   | 0.9741    | 0.9667      | 0.9733      | 0.9727 | 0.9989 | 0.9054 | 0.94   | [[146, 4], [1, 29]] |
| 0.207         | 6.0   | 2256 | 0.1922          | 0.9833   | 0.9832    | 0.9333      | 0.9933      | 0.9832 | 0.9991 | 0.9394 | 0.9267 | [[149, 1], [2, 28]] |
| 0.1677        | 7.0   | 2632 | 0.1783          | 0.9889   | 0.9889    | 0.9667      | 0.9933      | 0.9889 | 0.9991 | 0.96   | 0.96   | [[149, 1], [1, 29]] |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1