---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: swinv2-base-patch4-window12to16-192to256-22kto1k-ft-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-window12to16-192to256-22kto1k-ft-finetuned-galaxy10-decals

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6138
- Accuracy: 0.8653
- Precision: 0.8633
- Recall: 0.8653
- F1: 0.8633

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1028        | 0.99  | 62   | 0.8747          | 0.6815   | 0.7019    | 0.6815 | 0.6725 |
| 0.7637        | 2.0   | 125  | 0.6110          | 0.7993   | 0.8032    | 0.7993 | 0.7944 |
| 0.702         | 2.99  | 187  | 0.5407          | 0.8179   | 0.8282    | 0.8179 | 0.8201 |
| 0.6131        | 4.0   | 250  | 0.5038          | 0.8326   | 0.8356    | 0.8326 | 0.8276 |
| 0.5453        | 4.99  | 312  | 0.4523          | 0.8534   | 0.8547    | 0.8534 | 0.8528 |
| 0.5409        | 6.0   | 375  | 0.4908          | 0.8377   | 0.8389    | 0.8377 | 0.8339 |
| 0.5246        | 6.99  | 437  | 0.4583          | 0.8478   | 0.8509    | 0.8478 | 0.8486 |
| 0.478         | 8.0   | 500  | 0.4417          | 0.8506   | 0.8529    | 0.8506 | 0.8486 |
| 0.4845        | 8.99  | 562  | 0.4344          | 0.8596   | 0.8591    | 0.8596 | 0.8565 |
| 0.4228        | 10.0  | 625  | 0.4580          | 0.8478   | 0.8488    | 0.8478 | 0.8462 |
| 0.4414        | 10.99 | 687  | 0.4520          | 0.8534   | 0.8539    | 0.8534 | 0.8525 |
| 0.3783        | 12.0  | 750  | 0.4776          | 0.8517   | 0.8504    | 0.8517 | 0.8501 |
| 0.407         | 12.99 | 812  | 0.4800          | 0.8478   | 0.8482    | 0.8478 | 0.8444 |
| 0.3944        | 14.0  | 875  | 0.4541          | 0.8630   | 0.8639    | 0.8630 | 0.8618 |
| 0.3563        | 14.99 | 937  | 0.4848          | 0.8534   | 0.8531    | 0.8534 | 0.8523 |
| 0.3576        | 16.0  | 1000 | 0.4877          | 0.8540   | 0.8526    | 0.8540 | 0.8522 |
| 0.317         | 16.99 | 1062 | 0.5122          | 0.8551   | 0.8572    | 0.8551 | 0.8546 |
| 0.3439        | 18.0  | 1125 | 0.5073          | 0.8484   | 0.8509    | 0.8484 | 0.8466 |
| 0.3199        | 18.99 | 1187 | 0.5183          | 0.8574   | 0.8552    | 0.8574 | 0.8555 |
| 0.3121        | 20.0  | 1250 | 0.5367          | 0.8484   | 0.8471    | 0.8484 | 0.8451 |
| 0.2942        | 20.99 | 1312 | 0.5905          | 0.8534   | 0.8506    | 0.8534 | 0.8509 |
| 0.3253        | 22.0  | 1375 | 0.5762          | 0.8495   | 0.8498    | 0.8495 | 0.8478 |
| 0.2917        | 22.99 | 1437 | 0.5865          | 0.8433   | 0.8452    | 0.8433 | 0.8428 |
| 0.2708        | 24.0  | 1500 | 0.5802          | 0.8568   | 0.8532    | 0.8568 | 0.8539 |
| 0.2801        | 24.99 | 1562 | 0.6005          | 0.8557   | 0.8521    | 0.8557 | 0.8525 |
| 0.2608        | 26.0  | 1625 | 0.5916          | 0.8636   | 0.8606    | 0.8636 | 0.8612 |
| 0.2625        | 26.99 | 1687 | 0.5932          | 0.8568   | 0.8551    | 0.8568 | 0.8552 |
| 0.2759        | 28.0  | 1750 | 0.6277          | 0.8568   | 0.8557    | 0.8568 | 0.8546 |
| 0.2483        | 28.99 | 1812 | 0.6055          | 0.8630   | 0.8607    | 0.8630 | 0.8608 |
| 0.2554        | 29.76 | 1860 | 0.6138          | 0.8653   | 0.8633    | 0.8653 | 0.8633 |


### Framework versions

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