---
license: apache-2.0
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: 0.8247422680412371
---

<!-- 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: 0.5465
- Accuracy: 0.8247

## 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 7    | 1.2679          | 0.2990   |
| 1.3643        | 2.0   | 14   | 1.1288          | 0.5258   |
| 1.0267        | 3.0   | 21   | 0.6534          | 0.7010   |
| 1.0267        | 4.0   | 28   | 0.6587          | 0.7629   |
| 0.6635        | 5.0   | 35   | 0.7360          | 0.6701   |
| 0.5462        | 6.0   | 42   | 0.6479          | 0.7320   |
| 0.5462        | 7.0   | 49   | 0.5546          | 0.7835   |
| 0.4471        | 8.0   | 56   | 0.5583          | 0.7835   |
| 0.3094        | 9.0   | 63   | 0.5257          | 0.8247   |
| 0.242         | 10.0  | 70   | 0.5465          | 0.8247   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3