---
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: 0.7722370456736698
---

<!-- 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: 0.4877
- Accuracy: 0.7722

## 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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5611        | 1.0   | 392   | 0.5374          | 0.7341   |
| 0.5299        | 2.0   | 784   | 0.5180          | 0.7486   |
| 0.5289        | 3.0   | 1176  | 0.5049          | 0.7568   |
| 0.5208        | 4.0   | 1568  | 0.4980          | 0.7622   |
| 0.5051        | 5.0   | 1960  | 0.4996          | 0.7621   |
| 0.5035        | 6.0   | 2352  | 0.4890          | 0.7672   |
| 0.5028        | 7.0   | 2744  | 0.4880          | 0.7685   |
| 0.5129        | 8.0   | 3136  | 0.4966          | 0.7644   |
| 0.5014        | 9.0   | 3528  | 0.4895          | 0.7669   |
| 0.4923        | 10.0  | 3920  | 0.4880          | 0.7702   |
| 0.496         | 11.0  | 4312  | 0.4932          | 0.7673   |
| 0.4978        | 12.0  | 4704  | 0.4868          | 0.7718   |
| 0.4993        | 13.0  | 5096  | 0.4827          | 0.7723   |
| 0.4928        | 14.0  | 5488  | 0.4826          | 0.7724   |
| 0.4883        | 15.0  | 5880  | 0.4826          | 0.7729   |
| 0.4951        | 16.0  | 6272  | 0.4815          | 0.7717   |
| 0.4955        | 17.0  | 6664  | 0.4879          | 0.7700   |
| 0.4931        | 18.0  | 7056  | 0.4837          | 0.7720   |
| 0.4803        | 19.0  | 7448  | 0.4841          | 0.7732   |
| 0.4906        | 20.0  | 7840  | 0.4812          | 0.7737   |
| 0.4718        | 21.0  | 8232  | 0.4880          | 0.7731   |
| 0.479         | 22.0  | 8624  | 0.4826          | 0.7733   |
| 0.483         | 23.0  | 9016  | 0.4825          | 0.7719   |
| 0.4748        | 24.0  | 9408  | 0.4828          | 0.7738   |
| 0.4708        | 25.0  | 9800  | 0.4877          | 0.7722   |
| 0.4746        | 26.0  | 10192 | 0.4856          | 0.7734   |
| 0.4659        | 27.0  | 10584 | 0.4879          | 0.7725   |
| 0.4732        | 28.0  | 10976 | 0.4864          | 0.7721   |
| 0.4672        | 29.0  | 11368 | 0.4866          | 0.7725   |
| 0.4677        | 30.0  | 11760 | 0.4877          | 0.7722   |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3