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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# swinv2-base-patch4-window16-256-finetuned-galaxy10-decals

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co./microsoft/swinv2-base-patch4-window16-256) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4341
- Accuracy: 0.8574
- Precision: 0.8589
- Recall: 0.8574
- F1: 0.8546

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5098        | 0.99  | 62   | 1.2358          | 0.5569   | 0.5493    | 0.5569 | 0.5321 |
| 0.8845        | 2.0   | 125  | 0.7391          | 0.7599   | 0.7800    | 0.7599 | 0.7497 |
| 0.753         | 2.99  | 187  | 0.5997          | 0.7971   | 0.8062    | 0.7971 | 0.7903 |
| 0.6149        | 4.0   | 250  | 0.4920          | 0.8331   | 0.8285    | 0.8331 | 0.8276 |
| 0.5807        | 4.99  | 312  | 0.4623          | 0.8326   | 0.8323    | 0.8326 | 0.8315 |
| 0.5938        | 6.0   | 375  | 0.4857          | 0.8365   | 0.8403    | 0.8365 | 0.8294 |
| 0.5583        | 6.99  | 437  | 0.4680          | 0.8264   | 0.8314    | 0.8264 | 0.8243 |
| 0.5103        | 8.0   | 500  | 0.4882          | 0.8191   | 0.8312    | 0.8191 | 0.8180 |
| 0.5186        | 8.99  | 562  | 0.4341          | 0.8574   | 0.8589    | 0.8574 | 0.8546 |
| 0.4696        | 10.0  | 625  | 0.4293          | 0.8495   | 0.8484    | 0.8495 | 0.8481 |
| 0.4711        | 10.99 | 687  | 0.4396          | 0.8422   | 0.8431    | 0.8422 | 0.8414 |
| 0.4271        | 12.0  | 750  | 0.4547          | 0.8489   | 0.8500    | 0.8489 | 0.8480 |
| 0.4576        | 12.99 | 812  | 0.4424          | 0.8489   | 0.8522    | 0.8489 | 0.8473 |
| 0.4483        | 14.0  | 875  | 0.4355          | 0.8495   | 0.8531    | 0.8495 | 0.8492 |
| 0.3914        | 14.99 | 937  | 0.4360          | 0.8540   | 0.8533    | 0.8540 | 0.8532 |
| 0.3883        | 16.0  | 1000 | 0.4464          | 0.8546   | 0.8550    | 0.8546 | 0.8526 |
| 0.3421        | 16.99 | 1062 | 0.4473          | 0.8489   | 0.8486    | 0.8489 | 0.8479 |
| 0.3666        | 18.0  | 1125 | 0.4455          | 0.8540   | 0.8541    | 0.8540 | 0.8528 |
| 0.3737        | 18.99 | 1187 | 0.4587          | 0.8574   | 0.8560    | 0.8574 | 0.8561 |
| 0.3694        | 20.0  | 1250 | 0.4583          | 0.8551   | 0.8528    | 0.8551 | 0.8523 |
| 0.3269        | 20.99 | 1312 | 0.4883          | 0.8506   | 0.8494    | 0.8506 | 0.8487 |
| 0.3699        | 22.0  | 1375 | 0.4808          | 0.8501   | 0.8514    | 0.8501 | 0.8486 |
| 0.3395        | 22.99 | 1437 | 0.4706          | 0.8484   | 0.8493    | 0.8484 | 0.8477 |
| 0.3147        | 24.0  | 1500 | 0.4676          | 0.8568   | 0.8556    | 0.8568 | 0.8557 |
| 0.3352        | 24.99 | 1562 | 0.4868          | 0.8557   | 0.8543    | 0.8557 | 0.8538 |
| 0.3007        | 26.0  | 1625 | 0.4887          | 0.8489   | 0.8492    | 0.8489 | 0.8475 |
| 0.3049        | 26.99 | 1687 | 0.4838          | 0.8534   | 0.8532    | 0.8534 | 0.8526 |
| 0.3228        | 28.0  | 1750 | 0.4910          | 0.8551   | 0.8539    | 0.8551 | 0.8536 |
| 0.3005        | 28.99 | 1812 | 0.4846          | 0.8534   | 0.8517    | 0.8534 | 0.8518 |
| 0.2972        | 29.76 | 1860 | 0.4826          | 0.8557   | 0.8544    | 0.8557 | 0.8543 |


### Framework versions

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