---
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: 0.8327974276527331
    - name: Precision
      type: precision
      value: 0.860997154156041
    - name: Recall
      type: recall
      value: 0.8327974276527331
    - name: F1
      type: f1
      value: 0.8137864007121186
---

<!-- 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: 0.3277
- Accuracy: 0.8328
- Precision: 0.8610
- Recall: 0.8328
- F1: 0.8138

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5603        | 1.0   | 22   | 0.5610          | 0.6817   | 0.7833    | 0.6817 | 0.5559 |
| 0.3934        | 2.0   | 44   | 0.3976          | 0.7910   | 0.7879    | 0.7910 | 0.7768 |
| 0.2992        | 3.0   | 66   | 0.3036          | 0.8071   | 0.8044    | 0.8071 | 0.7965 |
| 0.2746        | 4.0   | 88   | 0.3538          | 0.7878   | 0.7812    | 0.7878 | 0.7799 |
| 0.2573        | 5.0   | 110  | 0.2242          | 0.8521   | 0.8561    | 0.8521 | 0.8535 |
| 0.2724        | 6.0   | 132  | 0.3801          | 0.7749   | 0.8145    | 0.7749 | 0.7347 |
| 0.2344        | 7.0   | 154  | 0.3327          | 0.8232   | 0.8544    | 0.8232 | 0.8011 |
| 0.2225        | 8.0   | 176  | 0.3736          | 0.8392   | 0.8655    | 0.8392 | 0.8221 |
| 0.225         | 9.0   | 198  | 0.3479          | 0.8328   | 0.8610    | 0.8328 | 0.8138 |
| 0.2308        | 10.0  | 220  | 0.3277          | 0.8328   | 0.8610    | 0.8328 | 0.8138 |


### Framework versions

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