---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swinv2-small-patch4-window16-256-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: 0.9892592592592593
    - name: F1
      type: f1
      value: 0.9892542163878574
    - name: Precision
      type: precision
      value: 0.9892896521886161
    - name: Recall
      type: recall
      value: 0.9892592592592593
---

<!-- 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-small-patch4-window16-256-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0328
- Accuracy: 0.9893
- F1: 0.9893
- Precision: 0.9893
- Recall: 0.9893

## 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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2326        | 1.0   | 253  | 0.0870          | 0.9715   | 0.9716 | 0.9720    | 0.9715 |
| 0.1955        | 2.0   | 506  | 0.0576          | 0.9789   | 0.9788 | 0.9794    | 0.9789 |
| 0.1229        | 3.0   | 759  | 0.0450          | 0.9837   | 0.9837 | 0.9839    | 0.9837 |
| 0.0797        | 4.0   | 1012 | 0.0332          | 0.9889   | 0.9889 | 0.9889    | 0.9889 |
| 0.0826        | 5.0   | 1265 | 0.0328          | 0.9893   | 0.9893 | 0.9893    | 0.9893 |


### Framework versions

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