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---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window16-256
tags:
- image-classification
- vision
- 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 the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4406
- Accuracy: 0.8602
- Precision: 0.8577
- Recall: 0.8602
- F1: 0.8585
## 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
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