---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
  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: 0.9714285714285714
    - name: Precision
      type: precision
      value: 0.9696825396825397
    - name: Recall
      type: recall
      value: 0.9714285714285714
    - name: F1
      type: f1
      value: 0.9695078031212484
---

<!-- 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-finetuned-eurosat

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: 0.0814
- Accuracy: 0.9714
- Precision: 0.9697
- Recall: 0.9714
- F1: 0.9695

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2262        | 0.9888 | 22   | 0.2061          | 0.9365   | 0.8770    | 0.9365 | 0.9058 |
| 0.1666        | 1.9775 | 44   | 0.1274          | 0.9333   | 0.8769    | 0.9333 | 0.9042 |
| 0.1168        | 2.9663 | 66   | 0.1054          | 0.9524   | 0.9461    | 0.9524 | 0.9438 |
| 0.0984        | 4.0    | 89   | 0.0824          | 0.9619   | 0.9591    | 0.9619 | 0.9599 |
| 0.1028        | 4.9888 | 111  | 0.0814          | 0.9714   | 0.9697    | 0.9714 | 0.9695 |
| 0.1082        | 5.9775 | 133  | 0.0835          | 0.9492   | 0.9518    | 0.9492 | 0.9329 |
| 0.0962        | 6.9663 | 155  | 0.0872          | 0.9587   | 0.9578    | 0.9587 | 0.9582 |
| 0.0799        | 8.0    | 178  | 0.0803          | 0.9587   | 0.9543    | 0.9587 | 0.9546 |
| 0.0954        | 8.9888 | 200  | 0.0685          | 0.9619   | 0.9584    | 0.9619 | 0.9587 |
| 0.0771        | 9.8876 | 220  | 0.0711          | 0.9619   | 0.9584    | 0.9619 | 0.9587 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1