---
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: 0.808641975308642
---

<!-- 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: 0.5712
- Accuracy: 0.8086

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.87  | 5    | 1.3767          | 0.5370   |
| 1.289         | 1.91  | 11   | 1.3503          | 0.5494   |
| 1.289         | 2.96  | 17   | 1.3712          | 0.5556   |
| 1.0376        | 4.0   | 23   | 1.3064          | 0.5556   |
| 1.0376        | 4.87  | 28   | 1.1062          | 0.5802   |
| 0.8346        | 5.91  | 34   | 0.9249          | 0.6481   |
| 0.7096        | 6.96  | 40   | 0.8947          | 0.6235   |
| 0.7096        | 8.0   | 46   | 0.8626          | 0.6543   |
| 0.6356        | 8.87  | 51   | 0.6820          | 0.7222   |
| 0.6356        | 9.91  | 57   | 0.7249          | 0.7346   |
| 0.5956        | 10.96 | 63   | 0.6818          | 0.7407   |
| 0.5956        | 12.0  | 69   | 0.6111          | 0.7840   |
| 0.5534        | 12.87 | 74   | 0.6026          | 0.7778   |
| 0.519         | 13.91 | 80   | 0.6070          | 0.7901   |
| 0.519         | 14.96 | 86   | 0.5758          | 0.7963   |
| 0.5117        | 16.0  | 92   | 0.5791          | 0.7840   |
| 0.5117        | 16.87 | 97   | 0.5711          | 0.8025   |
| 0.4913        | 17.39 | 100  | 0.5712          | 0.8086   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2