---
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: 0.7167325428194994
    - name: F1
      type: f1
      value: 0.6734177215189874
    - name: Precision
      type: precision
      value: 0.5621301775147929
    - name: Recall
      type: recall
      value: 0.8396464646464646
---

<!-- 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: 0.5309
- Accuracy: 0.7167
- F1: 0.6734
- Precision: 0.5621
- Recall: 0.8396

## 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: 1e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6658        | 1.0   | 143  | 0.6313          | 0.6421   | 0.0512 | 0.3284    | 0.0278 |
| 0.5528        | 2.0   | 286  | 0.5433          | 0.6987   | 0.5396 | 0.5759    | 0.5076 |
| 0.417         | 3.0   | 429  | 0.4806          | 0.7273   | 0.6306 | 0.5962    | 0.6692 |
| 0.3738        | 4.0   | 572  | 0.4195          | 0.7668   | 0.6849 | 0.6461    | 0.7285 |
| 0.3385        | 5.0   | 715  | 0.5555          | 0.7141   | 0.6720 | 0.5591    | 0.8422 |
| 0.3274        | 6.0   | 858  | 0.5506          | 0.7159   | 0.6646 | 0.5638    | 0.8093 |
| 0.3176        | 7.0   | 1001 | 0.5309          | 0.7167   | 0.6734 | 0.5621    | 0.8396 |


### Framework versions

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