---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7133333333333334
    - name: Precision
      type: precision
      value: 0.6732516172965611
    - name: Recall
      type: recall
      value: 0.7133333333333334
---

<!-- 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. -->

# swin-tiny-patch4-window7-224

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: 0.5797
- Accuracy: 0.7133
- Precision: 0.6733
- Recall: 0.7133
- F1 Score: 0.6650

## 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: 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.5965          | 0.725    | 0.5256    | 0.725  | 0.6094   |
| No log        | 2.0   | 8    | 0.6045          | 0.7125   | 0.5795    | 0.7125 | 0.6104   |
| No log        | 3.0   | 12   | 0.5910          | 0.725    | 0.6645    | 0.725  | 0.6169   |
| 0.6165        | 4.0   | 16   | 0.5865          | 0.7333   | 0.7162    | 0.7333 | 0.6418   |
| 0.6165        | 5.0   | 20   | 0.5789          | 0.7292   | 0.6846    | 0.7292 | 0.6562   |
| 0.6165        | 6.0   | 24   | 0.5649          | 0.725    | 0.6702    | 0.725  | 0.6427   |
| 0.6165        | 7.0   | 28   | 0.5660          | 0.7375   | 0.7090    | 0.7375 | 0.6668   |
| 0.5966        | 8.0   | 32   | 0.5972          | 0.7375   | 0.7108    | 0.7375 | 0.7132   |
| 0.5966        | 9.0   | 36   | 0.5666          | 0.7417   | 0.7134    | 0.7417 | 0.6835   |
| 0.5966        | 10.0  | 40   | 0.5781          | 0.7417   | 0.7124    | 0.7417 | 0.7084   |
| 0.5966        | 11.0  | 44   | 0.6009          | 0.7083   | 0.6900    | 0.7083 | 0.6967   |
| 0.5921        | 12.0  | 48   | 0.5678          | 0.75     | 0.7244    | 0.75   | 0.7118   |
| 0.5921        | 13.0  | 52   | 0.5581          | 0.7583   | 0.7429    | 0.7583 | 0.7115   |
| 0.5921        | 14.0  | 56   | 0.5587          | 0.7542   | 0.7340    | 0.7542 | 0.7083   |
| 0.5847        | 15.0  | 60   | 0.5589          | 0.7542   | 0.7340    | 0.7542 | 0.7083   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3