---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swinv2-tiny-patch4-window8-256-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: 0.9825925925925926
    - name: F1
      type: f1
      value: 0.9825868474705166
    - name: Precision
      type: precision
      value: 0.9828193476192771
    - name: Recall
      type: recall
      value: 0.9825925925925926
---

<!-- 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. -->

# swinv2-tiny-patch4-window8-256-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0510
- Accuracy: 0.9826
- F1: 0.9826
- Precision: 0.9828
- Recall: 0.9826

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4479        | 1.0   | 95   | 0.1592          | 0.9478   | 0.9478 | 0.9500    | 0.9478 |
| 0.3078        | 2.0   | 190  | 0.0914          | 0.9685   | 0.9686 | 0.9695    | 0.9685 |
| 0.2307        | 3.0   | 285  | 0.0603          | 0.9785   | 0.9785 | 0.9790    | 0.9785 |
| 0.227         | 4.0   | 380  | 0.0531          | 0.9811   | 0.9811 | 0.9814    | 0.9811 |
| 0.1674        | 5.0   | 475  | 0.0510          | 0.9826   | 0.9826 | 0.9828    | 0.9826 |


### Framework versions

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