---
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: 0.6343582887700535
    - name: Precision
      type: precision
      value: 0.7715676584335054
    - name: Recall
      type: recall
      value: 0.6343582887700535
---

<!-- 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: 3.3036
- Accuracy: 0.6344
- Precision: 0.7716
- Recall: 0.6344
- Confusion Matrix: [[1498, 14], [1080, 400]]

## 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          |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-------------------------:|
| 0.1263        | 1.0   | 374  | 1.1458          | 0.7309   | 0.8125    | 0.7309 | [[1493, 19], [786, 694]]  |
| 0.0301        | 2.0   | 748  | 3.0924          | 0.6330   | 0.7754    | 0.6330 | [[1502, 10], [1088, 392]] |
| 0.0467        | 3.0   | 1122 | 3.3036          | 0.6344   | 0.7716    | 0.6344 | [[1498, 14], [1080, 400]] |


### Framework versions

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