---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
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: 0.9718236819360415
---

<!-- 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: 0.1356
- Accuracy: 0.9718

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5595        | 1.0   | 406  | 0.1957          | 0.9628   |
| 0.6314        | 2.0   | 813  | 0.1814          | 0.9654   |
| 0.5433        | 3.0   | 1220 | 0.1723          | 0.9658   |
| 0.488         | 4.0   | 1627 | 0.1588          | 0.9677   |
| 0.5789        | 5.0   | 2033 | 0.1572          | 0.9689   |
| 0.4526        | 6.0   | 2440 | 0.1496          | 0.9701   |
| 0.4538        | 7.0   | 2847 | 0.1447          | 0.9708   |
| 0.447         | 8.0   | 3254 | 0.1414          | 0.9710   |
| 0.3809        | 9.0   | 3660 | 0.1385          | 0.9715   |
| 0.4515        | 9.98  | 4060 | 0.1356          | 0.9718   |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0