---
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: 0.9365918097754293
---

<!-- 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: 0.2033
- Accuracy: 0.9366

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3125        | 0.9684 | 23   | 0.3341          | 0.8732   |
| 0.2977        | 1.9789 | 47   | 0.2943          | 0.9062   |
| 0.2677        | 2.9895 | 71   | 0.2374          | 0.9168   |
| 0.2483        | 4.0    | 95   | 0.2230          | 0.9207   |
| 0.2331        | 4.9684 | 118  | 0.2198          | 0.9234   |
| 0.2315        | 5.9789 | 142  | 0.2150          | 0.9181   |
| 0.2249        | 6.9895 | 166  | 0.2177          | 0.9234   |
| 0.1683        | 8.0    | 190  | 0.2068          | 0.9326   |
| 0.1725        | 8.9684 | 213  | 0.2040          | 0.9366   |
| 0.1789        | 9.6842 | 230  | 0.2033          | 0.9366   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1