---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-small-patch4-window8-256-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: 0.6818181818181818
---

<!-- 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-small-patch4-window8-256-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window8-256](https://huggingface.co/microsoft/swinv2-small-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2850
- Accuracy: 0.6818

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.91  | 5    | 0.5918          | 0.7338   |
| 0.6689        | 2.0   | 11   | 0.6330          | 0.7013   |
| 0.6689        | 2.91  | 16   | 0.6016          | 0.7013   |
| 0.5503        | 4.0   | 22   | 0.6296          | 0.7143   |
| 0.5503        | 4.91  | 27   | 0.7059          | 0.5455   |
| 0.3911        | 6.0   | 33   | 0.8002          | 0.5260   |
| 0.3911        | 6.91  | 38   | 0.7831          | 0.7013   |
| 0.2624        | 8.0   | 44   | 0.7862          | 0.6429   |
| 0.2624        | 8.91  | 49   | 1.0588          | 0.5649   |
| 0.1057        | 10.0  | 55   | 0.9249          | 0.6429   |
| 0.0644        | 10.91 | 60   | 1.0545          | 0.6558   |
| 0.0644        | 12.0  | 66   | 1.0990          | 0.6494   |
| 0.0265        | 12.91 | 71   | 1.1942          | 0.6234   |
| 0.0265        | 14.0  | 77   | 1.2192          | 0.6429   |
| 0.0152        | 14.91 | 82   | 1.2414          | 0.6623   |
| 0.0152        | 16.0  | 88   | 1.3708          | 0.6299   |
| 0.0146        | 16.91 | 93   | 1.3162          | 0.6558   |
| 0.0146        | 18.0  | 99   | 1.2849          | 0.6818   |
| 0.0204        | 18.18 | 100  | 1.2850          | 0.6818   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2