---
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: 0.8405797101449275
---

<!-- 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: 0.4560
- Accuracy: 0.8406

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7738        | 1.0   | 13   | 1.3796          | 0.5845   |
| 1.2319        | 2.0   | 26   | 1.1322          | 0.6039   |
| 1.0355        | 3.0   | 39   | 0.9885          | 0.6232   |
| 0.7439        | 4.0   | 52   | 1.2022          | 0.6232   |
| 0.6792        | 5.0   | 65   | 0.7238          | 0.7246   |
| 0.6195        | 6.0   | 78   | 0.7041          | 0.7536   |
| 0.5151        | 7.0   | 91   | 0.6132          | 0.7826   |
| 0.556         | 8.0   | 104  | 0.6381          | 0.7488   |
| 0.4727        | 9.0   | 117  | 0.6127          | 0.7923   |
| 0.4879        | 10.0  | 130  | 0.4921          | 0.8551   |
| 0.436         | 11.0  | 143  | 0.5578          | 0.7923   |
| 0.3781        | 12.0  | 156  | 0.5095          | 0.8261   |
| 0.4201        | 13.0  | 169  | 0.5151          | 0.8454   |
| 0.3773        | 14.0  | 182  | 0.4612          | 0.8261   |
| 0.3611        | 15.0  | 195  | 0.5384          | 0.7971   |
| 0.3855        | 16.0  | 208  | 0.5267          | 0.8261   |
| 0.3926        | 17.0  | 221  | 0.4100          | 0.8647   |
| 0.3513        | 18.0  | 234  | 0.4508          | 0.8454   |
| 0.3389        | 19.0  | 247  | 0.4420          | 0.8502   |
| 0.3232        | 20.0  | 260  | 0.4560          | 0.8406   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1