---
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: 0.9764705882352941
---

<!-- 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: 0.0625
- Accuracy: 0.9765

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.506         | 1.0   | 30   | 1.1397          | 0.5976   |
| 0.5645        | 2.0   | 60   | 0.3396          | 0.88     |
| 0.4507        | 3.0   | 90   | 0.1972          | 0.9247   |
| 0.418         | 4.0   | 120  | 0.1484          | 0.9506   |
| 0.3169        | 5.0   | 150  | 0.1866          | 0.92     |
| 0.3346        | 6.0   | 180  | 0.0973          | 0.9718   |
| 0.2823        | 7.0   | 210  | 0.0973          | 0.9694   |
| 0.2711        | 8.0   | 240  | 0.0805          | 0.9671   |
| 0.2638        | 9.0   | 270  | 0.0749          | 0.9718   |
| 0.2755        | 10.0  | 300  | 0.0625          | 0.9765   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2