---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: Augmented
      split: train
      args: Augmented
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8795454545454545
---

<!-- 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. -->

# swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50

This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3293
- Accuracy: 0.8795

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8498        | 1.0   | 55   | 1.7348          | 0.3273   |
| 1.1886        | 2.0   | 110  | 1.0198          | 0.6102   |
| 0.8636        | 3.0   | 165  | 0.6859          | 0.7398   |
| 0.576         | 4.0   | 220  | 0.4357          | 0.8477   |
| 0.5875        | 5.0   | 275  | 0.4188          | 0.8386   |
| 0.4677        | 6.0   | 330  | 0.3293          | 0.8795   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3