---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: swinv2-small-patch4-window8-256-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-window8-256-finetuned-galaxy10-decals

This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window8-256](https://huggingface.co/microsoft/swinv2-small-patch4-window8-256) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4138
- Accuracy: 0.8647
- Precision: 0.8650
- Recall: 0.8647
- F1: 0.8612

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6991        | 0.99  | 62   | 1.4106          | 0.5011   | 0.4620    | 0.5011 | 0.4641 |
| 0.9843        | 2.0   | 125  | 0.8254          | 0.7148   | 0.7390    | 0.7148 | 0.7097 |
| 0.8115        | 2.99  | 187  | 0.6271          | 0.7773   | 0.7700    | 0.7773 | 0.7671 |
| 0.6956        | 4.0   | 250  | 0.5679          | 0.8061   | 0.8029    | 0.8061 | 0.7967 |
| 0.6167        | 4.99  | 312  | 0.5484          | 0.8281   | 0.8271    | 0.8281 | 0.8247 |
| 0.6291        | 6.0   | 375  | 0.5184          | 0.8191   | 0.8241    | 0.8191 | 0.8123 |
| 0.6113        | 6.99  | 437  | 0.5175          | 0.8134   | 0.8149    | 0.8134 | 0.8097 |
| 0.5468        | 8.0   | 500  | 0.4897          | 0.8309   | 0.8363    | 0.8309 | 0.8283 |
| 0.567         | 8.99  | 562  | 0.4459          | 0.8568   | 0.8594    | 0.8568 | 0.8529 |
| 0.5449        | 10.0  | 625  | 0.4544          | 0.8393   | 0.8390    | 0.8393 | 0.8353 |
| 0.5437        | 10.99 | 687  | 0.4528          | 0.8388   | 0.8410    | 0.8388 | 0.8375 |
| 0.4754        | 12.0  | 750  | 0.4524          | 0.8422   | 0.8421    | 0.8422 | 0.8396 |
| 0.5121        | 12.99 | 812  | 0.4840          | 0.8382   | 0.8415    | 0.8382 | 0.8349 |
| 0.5074        | 14.0  | 875  | 0.4138          | 0.8647   | 0.8650    | 0.8647 | 0.8612 |
| 0.4567        | 14.99 | 937  | 0.4339          | 0.8484   | 0.8479    | 0.8484 | 0.8473 |
| 0.4686        | 16.0  | 1000 | 0.4391          | 0.8540   | 0.8521    | 0.8540 | 0.8504 |
| 0.414         | 16.99 | 1062 | 0.4626          | 0.8422   | 0.8443    | 0.8422 | 0.8388 |
| 0.4382        | 18.0  | 1125 | 0.4116          | 0.8568   | 0.8558    | 0.8568 | 0.8541 |
| 0.4322        | 18.99 | 1187 | 0.4506          | 0.8512   | 0.8529    | 0.8512 | 0.8496 |
| 0.4424        | 20.0  | 1250 | 0.4300          | 0.8568   | 0.8542    | 0.8568 | 0.8538 |
| 0.4062        | 20.99 | 1312 | 0.4609          | 0.8608   | 0.8597    | 0.8608 | 0.8578 |
| 0.4459        | 22.0  | 1375 | 0.4517          | 0.8568   | 0.8580    | 0.8568 | 0.8551 |
| 0.4109        | 22.99 | 1437 | 0.4490          | 0.8534   | 0.8534    | 0.8534 | 0.8513 |
| 0.3984        | 24.0  | 1500 | 0.4434          | 0.8619   | 0.8606    | 0.8619 | 0.8601 |
| 0.4034        | 24.99 | 1562 | 0.4613          | 0.8596   | 0.8577    | 0.8596 | 0.8571 |
| 0.3682        | 26.0  | 1625 | 0.4493          | 0.8596   | 0.8591    | 0.8596 | 0.8573 |
| 0.3779        | 26.99 | 1687 | 0.4366          | 0.8591   | 0.8581    | 0.8591 | 0.8575 |
| 0.3965        | 28.0  | 1750 | 0.4370          | 0.8636   | 0.8616    | 0.8636 | 0.8609 |
| 0.3712        | 28.99 | 1812 | 0.4380          | 0.8591   | 0.8576    | 0.8591 | 0.8568 |
| 0.3776        | 29.76 | 1860 | 0.4389          | 0.8630   | 0.8624    | 0.8630 | 0.8612 |


### Framework versions

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