transformers pytorch swin image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swin-tiny-patch4-window7-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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: 0.9920562868815251
---

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# 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: 0.0410
- Accuracy: 0.9921

## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2347        | 1.0   | 428  | 0.0824          | 0.9721   |
| 0.217         | 2.0   | 857  | 0.0906          | 0.9768   |
| 0.1023        | 3.0   | 1285 | 0.0561          | 0.9866   |
| 0.1007        | 4.0   | 1714 | 0.0429          | 0.9902   |
| 0.0687        | 5.0   | 2142 | 0.0472          | 0.9877   |
| 0.09          | 6.0   | 2571 | 0.0484          | 0.9884   |
| 0.1087        | 7.0   | 2999 | 0.0409          | 0.9898   |
| 0.0477        | 8.0   | 3428 | 0.0468          | 0.9909   |
| 0.0677        | 9.0   | 3856 | 0.0410          | 0.9921   |
| 0.0315        | 9.99  | 4280 | 0.0426          | 0.9907   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.16.0
- Tokenizers 0.13.3
