---
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: 1.0
---

<!-- 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: 0.0004
- Accuracy: 1.0

## 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: 3e-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: 0.05
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5952        | 1.0   | 55   | 0.8490          | 0.6693   |
| 0.7582        | 2.0   | 110  | 0.4561          | 0.8386   |
| 0.4359        | 3.0   | 165  | 0.2408          | 0.9227   |
| 0.318         | 4.0   | 220  | 0.1294          | 0.9568   |
| 0.2414        | 5.0   | 275  | 0.0346          | 0.9909   |
| 0.1888        | 6.0   | 330  | 0.0419          | 0.9864   |
| 0.1717        | 7.0   | 385  | 0.0238          | 0.9943   |
| 0.1785        | 8.0   | 440  | 0.0230          | 0.9943   |
| 0.1654        | 9.0   | 495  | 0.0076          | 1.0      |
| 0.1322        | 10.0  | 550  | 0.0046          | 1.0      |
| 0.1123        | 11.0  | 605  | 0.0035          | 1.0      |
| 0.0953        | 12.0  | 660  | 0.0025          | 1.0      |
| 0.0864        | 13.0  | 715  | 0.0033          | 1.0      |
| 0.0984        | 14.0  | 770  | 0.0033          | 0.9989   |
| 0.0952        | 15.0  | 825  | 0.0015          | 1.0      |
| 0.0678        | 16.0  | 880  | 0.0022          | 1.0      |
| 0.0592        | 17.0  | 935  | 0.0013          | 1.0      |
| 0.0729        | 18.0  | 990  | 0.0037          | 0.9989   |
| 0.0672        | 19.0  | 1045 | 0.0041          | 0.9989   |
| 0.0615        | 20.0  | 1100 | 0.0010          | 1.0      |
| 0.058         | 21.0  | 1155 | 0.0009          | 1.0      |
| 0.0571        | 22.0  | 1210 | 0.0021          | 0.9989   |
| 0.0755        | 23.0  | 1265 | 0.0022          | 0.9989   |
| 0.0688        | 24.0  | 1320 | 0.0025          | 0.9989   |
| 0.0417        | 25.0  | 1375 | 0.0003          | 1.0      |
| 0.0589        | 26.0  | 1430 | 0.0007          | 1.0      |
| 0.0563        | 27.0  | 1485 | 0.0007          | 1.0      |
| 0.0603        | 28.0  | 1540 | 0.0010          | 0.9989   |
| 0.0469        | 29.0  | 1595 | 0.0005          | 1.0      |
| 0.0525        | 30.0  | 1650 | 0.0004          | 1.0      |


### Framework versions

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