---
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: 0.7090909090909091
---

<!-- 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: 0.6671
- Accuracy: 0.7091

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1158        | 0.97  | 15   | 0.9997          | 0.5045   |
| 0.8261        | 2.0   | 31   | 0.9180          | 0.5909   |
| 0.7361        | 2.97  | 46   | 0.8047          | 0.65     |
| 0.6325        | 4.0   | 62   | 0.7320          | 0.6818   |
| 0.5946        | 4.97  | 77   | 0.7196          | 0.6773   |
| 0.5149        | 6.0   | 93   | 0.6827          | 0.7273   |
| 0.5083        | 6.97  | 108  | 0.6906          | 0.6955   |
| 0.4316        | 8.0   | 124  | 0.6681          | 0.7091   |
| 0.4214        | 8.97  | 139  | 0.6700          | 0.7091   |
| 0.4096        | 9.68  | 150  | 0.6671          | 0.7091   |


### Framework versions

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