---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- 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: 0.9883021390374331
    - name: Precision
      type: precision
      value: 0.9883071765108582
    - name: Recall
      type: recall
      value: 0.9883021390374331
---

<!-- 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: 0.0394
- Accuracy: 0.9883
- Precision: 0.9883
- Recall: 0.9883
- Confusion Matrix: [[1497, 15], [20, 1460]]

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Confusion Matrix         |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------------------------:|
| 0.1107        | 1.0   | 374  | 0.0641          | 0.9786   | 0.9787    | 0.9786 | [[1488, 24], [40, 1440]] |
| 0.1079        | 2.0   | 748  | 0.0560          | 0.9773   | 0.9776    | 0.9773 | [[1498, 14], [54, 1426]] |
| 0.0624        | 3.0   | 1122 | 0.0394          | 0.9883   | 0.9883    | 0.9883 | [[1497, 15], [20, 1460]] |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0