---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
- nielsr/eurosat-demo
metrics:
- accuracy
widget:
- src: https://drive.google.com/uc?id=1trKgvkMRQ3BB0VcqnDwmieLxXhWmS8rq
  example_title: Annual Crop
- src: https://drive.google.com/uc?id=1kWQbPNHVa_JscS0age5E0UOSBcU1bh18
  example_title: Forest
- src: https://drive.google.com/uc?id=12YbxF-MfpMqLPB91HuTPEgcg1xnZKhGP
  example_title: Herbaceous Vegetation
- src: https://drive.google.com/uc?id=1NkzDiaQ1ciMDf89C8uA5zGx984bwkFCi
  example_title: Highway
- src: https://drive.google.com/uc?id=1F6r7O0rlgzaPvY6XBpFOWUTIddEIUkxx
  example_title: Industrial
- src: https://drive.google.com/uc?id=16zOtFHZ9E17jA9Ua4PsXrUjugSs77XKm
  example_title: Pasture
- src: https://drive.google.com/uc?id=163tqIdoVY7WFtKQlpz_bPM9WjwbJAtd
  example_title: Permanent Crop
- src: https://drive.google.com/uc?id=1qsX-XsrE3dMp7C7LLVa6HriaABIXuBrJ
  example_title: Residential
- src: https://drive.google.com/uc?id=1UK2praQHbNXDnctJt58rrlQZu84lxyk
  example_title: River
- src: https://drive.google.com/uc?id=1zVAfR7N5hXy6eq1cVOd8bXPjC1sqxVir
  example_title: Sea Lake
base_model: microsoft/swin-tiny-patch4-window7-224
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: image_folder
      type: image_folder
      args: default
    metrics:
    - type: accuracy
      value: 0.9848148148148148
      name: Accuracy
---

<!-- 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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0536
- Accuracy: 0.9848

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2602        | 1.0   | 190  | 0.1310          | 0.9563   |
| 0.1975        | 2.0   | 380  | 0.1063          | 0.9637   |
| 0.142         | 3.0   | 570  | 0.0642          | 0.9767   |
| 0.1235        | 4.0   | 760  | 0.0560          | 0.9837   |
| 0.1019        | 5.0   | 950  | 0.0536          | 0.9848   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1