---
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: 0.8842443729903537
    - name: Precision
      type: precision
      value: 0.8965561746996403
    - name: Recall
      type: recall
      value: 0.8842443729903537
    - name: F1
      type: f1
      value: 0.8866264991906878
---

<!-- 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: 0.2001
- Accuracy: 0.8842
- Precision: 0.8966
- Recall: 0.8842
- F1: 0.8866

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2604        | 1.0   | 22   | 0.2334          | 0.8392   | 0.8375    | 0.8392 | 0.8333 |
| 0.2492        | 2.0   | 44   | 0.2621          | 0.8264   | 0.8229    | 0.8264 | 0.8232 |
| 0.2373        | 3.0   | 66   | 0.2334          | 0.8617   | 0.8639    | 0.8617 | 0.8626 |
| 0.2623        | 4.0   | 88   | 0.2036          | 0.8810   | 0.8908    | 0.8810 | 0.8832 |
| 0.2378        | 5.0   | 110  | 0.2944          | 0.8199   | 0.8165    | 0.8199 | 0.8173 |
| 0.221         | 6.0   | 132  | 0.2027          | 0.8682   | 0.8752    | 0.8682 | 0.8701 |
| 0.2339        | 7.0   | 154  | 0.2291          | 0.8585   | 0.8573    | 0.8585 | 0.8577 |
| 0.2215        | 8.0   | 176  | 0.2732          | 0.8682   | 0.8685    | 0.8682 | 0.8683 |
| 0.2162        | 9.0   | 198  | 0.2260          | 0.8682   | 0.8713    | 0.8682 | 0.8693 |
| 0.2226        | 10.0  | 220  | 0.2001          | 0.8842   | 0.8966    | 0.8842 | 0.8866 |


### Framework versions

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