---
license: apache-2.0
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: SEMDataset
      split: train
      args: SEMDataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.782051282051282
---

<!-- 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: 0.5657
- Accuracy: 0.7821

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1465        | 0.97  | 16   | 1.8341          | 0.3462   |
| 1.7722        | 2.0   | 33   | 1.5865          | 0.4017   |
| 1.6005        | 2.97  | 49   | 1.4867          | 0.4060   |
| 1.429         | 4.0   | 66   | 1.3933          | 0.4487   |
| 1.2294        | 4.97  | 82   | 1.2696          | 0.5385   |
| 1.1224        | 6.0   | 99   | 1.2842          | 0.5641   |
| 0.9776        | 6.97  | 115  | 0.9923          | 0.6197   |
| 0.8678        | 8.0   | 132  | 1.1118          | 0.6368   |
| 0.8125        | 8.97  | 148  | 0.8974          | 0.6624   |
| 0.7022        | 10.0  | 165  | 0.8582          | 0.6838   |
| 0.6047        | 10.97 | 181  | 0.7019          | 0.7393   |
| 0.6223        | 12.0  | 198  | 0.6818          | 0.7308   |
| 0.5331        | 12.97 | 214  | 0.8265          | 0.7051   |
| 0.4995        | 14.0  | 231  | 0.6365          | 0.7521   |
| 0.4132        | 14.97 | 247  | 0.6585          | 0.7308   |
| 0.3978        | 16.0  | 264  | 0.5789          | 0.7692   |
| 0.3388        | 16.97 | 280  | 0.6038          | 0.7650   |
| 0.3376        | 18.0  | 297  | 0.5306          | 0.7821   |
| 0.3455        | 18.97 | 313  | 0.5797          | 0.7692   |
| 0.3207        | 19.39 | 320  | 0.5657          | 0.7821   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3