---
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: 0.8338171262699564
---

<!-- 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: 0.4653
- Accuracy: 0.8338

## 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 | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.8849        | 1.0   | 97   | 0.6836   | 0.8586          |
| 0.7461        | 2.0   | 194  | 0.7678   | 0.6177          |
| 0.6429        | 3.0   | 291  | 0.7881   | 0.5690          |
| 0.6522        | 4.0   | 388  | 0.7968   | 0.5325          |
| 0.6189        | 5.0   | 485  | 0.8084   | 0.5213          |
| 0.6045        | 6.0   | 582  | 0.5242   | 0.8099          |
| 0.5851        | 7.0   | 679  | 0.5081   | 0.8157          |
| 0.5268        | 8.0   | 776  | 0.4941   | 0.8193          |
| 0.5269        | 9.0   | 873  | 0.4589   | 0.8295          |
| 0.556         | 10.0  | 970  | 0.4653   | 0.8338          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0