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---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window16-256
tags:
- 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
should probably proofread and complete it, then remove this comment. -->
# 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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4826
- Accuracy: 0.8557
- Precision: 0.8544
- Recall: 0.8557
- F1: 0.8543
## 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
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