---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
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: 0.9844444444444445
    - name: F1
      type: f1
      value: 0.9844678306487884
    - name: Precision
      type: precision
      value: 0.9846508141836958
    - name: Recall
      type: recall
      value: 0.9844444444444445
---

<!-- 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: 0.0393
- Accuracy: 0.9844
- F1: 0.9845
- Precision: 0.9847
- Recall: 0.9844

## 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: 0.0001
- 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: 0.2
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3039        | 1.0   | 95   | 0.1300          | 0.9607   | 0.9609 | 0.9619    | 0.9607 |
| 0.2357        | 2.0   | 190  | 0.0815          | 0.9678   | 0.9678 | 0.9685    | 0.9678 |
| 0.163         | 3.0   | 285  | 0.0559          | 0.9807   | 0.9807 | 0.9809    | 0.9807 |
| 0.1267        | 4.0   | 380  | 0.0492          | 0.9837   | 0.9837 | 0.9839    | 0.9837 |
| 0.1059        | 5.0   | 475  | 0.0393          | 0.9844   | 0.9845 | 0.9847    | 0.9844 |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1