---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window16-256
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: swinv2-small-patch4-window16-256-finetuned-galaxy10-decals
  results: []
---

<!-- 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-window16-256-finetuned-galaxy10-decals

This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4737
- Accuracy: 0.8489
- Precision: 0.8486
- Recall: 0.8489
- F1: 0.8472

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6168        | 0.99  | 62   | 1.3397          | 0.5006   | 0.4880    | 0.5006 | 0.4599 |
| 0.9396        | 2.0   | 125  | 0.7823          | 0.7463   | 0.7602    | 0.7463 | 0.7410 |
| 0.782         | 2.99  | 187  | 0.5995          | 0.7948   | 0.7937    | 0.7948 | 0.7885 |
| 0.6373        | 4.0   | 250  | 0.5227          | 0.8230   | 0.8192    | 0.8230 | 0.8176 |
| 0.6047        | 4.99  | 312  | 0.5238          | 0.8281   | 0.8272    | 0.8281 | 0.8262 |
| 0.6143        | 6.0   | 375  | 0.5091          | 0.8348   | 0.8429    | 0.8348 | 0.8298 |
| 0.5805        | 6.99  | 437  | 0.4921          | 0.8264   | 0.8275    | 0.8264 | 0.8254 |
| 0.5476        | 8.0   | 500  | 0.4832          | 0.8320   | 0.8409    | 0.8320 | 0.8291 |
| 0.5333        | 8.99  | 562  | 0.4456          | 0.8501   | 0.8500    | 0.8501 | 0.8477 |
| 0.5062        | 10.0  | 625  | 0.4493          | 0.8467   | 0.8480    | 0.8467 | 0.8457 |
| 0.5001        | 10.99 | 687  | 0.4617          | 0.8450   | 0.8468    | 0.8450 | 0.8449 |
| 0.4572        | 12.0  | 750  | 0.4497          | 0.8467   | 0.8450    | 0.8467 | 0.8449 |
| 0.4681        | 12.99 | 812  | 0.4588          | 0.8489   | 0.8486    | 0.8489 | 0.8452 |
| 0.4747        | 14.0  | 875  | 0.4281          | 0.8529   | 0.8554    | 0.8529 | 0.8508 |
| 0.4283        | 14.99 | 937  | 0.4406          | 0.8602   | 0.8577    | 0.8602 | 0.8585 |
| 0.4296        | 16.0  | 1000 | 0.4458          | 0.8534   | 0.8512    | 0.8534 | 0.8498 |
| 0.3734        | 16.99 | 1062 | 0.4623          | 0.8416   | 0.8419    | 0.8416 | 0.8386 |
| 0.3921        | 18.0  | 1125 | 0.4438          | 0.8517   | 0.8506    | 0.8517 | 0.8496 |
| 0.3954        | 18.99 | 1187 | 0.4712          | 0.8467   | 0.8487    | 0.8467 | 0.8446 |
| 0.3995        | 20.0  | 1250 | 0.4648          | 0.8484   | 0.8467    | 0.8484 | 0.8448 |
| 0.3859        | 20.99 | 1312 | 0.4728          | 0.8495   | 0.8487    | 0.8495 | 0.8462 |
| 0.4046        | 22.0  | 1375 | 0.4720          | 0.8472   | 0.8467    | 0.8472 | 0.8453 |
| 0.3651        | 22.99 | 1437 | 0.4837          | 0.8416   | 0.8409    | 0.8416 | 0.8396 |
| 0.3481        | 24.0  | 1500 | 0.4742          | 0.8540   | 0.8522    | 0.8540 | 0.8524 |
| 0.3706        | 24.99 | 1562 | 0.4846          | 0.8478   | 0.8477    | 0.8478 | 0.8455 |
| 0.3278        | 26.0  | 1625 | 0.4798          | 0.8506   | 0.8502    | 0.8506 | 0.8484 |
| 0.3484        | 26.99 | 1687 | 0.4675          | 0.8529   | 0.8538    | 0.8529 | 0.8520 |
| 0.3626        | 28.0  | 1750 | 0.4768          | 0.8450   | 0.8446    | 0.8450 | 0.8429 |
| 0.3324        | 28.99 | 1812 | 0.4725          | 0.8484   | 0.8470    | 0.8484 | 0.8460 |
| 0.3462        | 29.76 | 1860 | 0.4737          | 0.8489   | 0.8486    | 0.8489 | 0.8472 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1