---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224-image-classifier
  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: 0.8080808080808081
    - name: F1
      type: f1
      value: 0.750428326670474
    - name: Precision
      type: precision
      value: 0.6850886339937435
    - name: Recall
      type: recall
      value: 0.8295454545454546
---

<!-- 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-image-classifier

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: 0.3204
- Accuracy: 0.8081
- F1: 0.7504
- Precision: 0.6851
- Recall: 0.8295

## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6377        | 1.0   | 143  | 0.6156          | 0.6421   | 0.5356 | 0.4881    | 0.5934 |
| 0.4556        | 2.0   | 286  | 0.4928          | 0.7141   | 0.6286 | 0.5734    | 0.6957 |
| 0.3616        | 3.0   | 429  | 0.5772          | 0.6895   | 0.5930 | 0.5450    | 0.6503 |
| 0.3582        | 4.0   | 572  | 0.3441          | 0.7835   | 0.6644 | 0.7208    | 0.6162 |
| 0.3374        | 5.0   | 715  | 0.4094          | 0.7699   | 0.7434 | 0.6072    | 0.9583 |
| 0.3273        | 6.0   | 858  | 0.6065          | 0.7115   | 0.6364 | 0.5665    | 0.7260 |
| 0.3091        | 7.0   | 1001 | 0.3204          | 0.8081   | 0.7504 | 0.6851    | 0.8295 |
| 0.3026        | 8.0   | 1144 | 0.3946          | 0.7694   | 0.6120 | 0.7380    | 0.5227 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3