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

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4552
- Accuracy: 0.8551
- Precision: 0.8529
- Recall: 0.8551
- F1: 0.8513

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7462        | 0.99  | 62   | 1.4592          | 0.4431   | 0.4309    | 0.4431 | 0.3967 |
| 1.1805        | 2.0   | 125  | 1.0335          | 0.6460   | 0.6741    | 0.6460 | 0.6241 |
| 0.9342        | 2.99  | 187  | 0.7051          | 0.7537   | 0.7478    | 0.7537 | 0.7394 |
| 0.786         | 4.0   | 250  | 0.6468          | 0.7745   | 0.7731    | 0.7745 | 0.7637 |
| 0.7062        | 4.99  | 312  | 0.6013          | 0.8038   | 0.8052    | 0.8038 | 0.8008 |
| 0.7011        | 6.0   | 375  | 0.5373          | 0.8123   | 0.8171    | 0.8123 | 0.8041 |
| 0.7014        | 6.99  | 437  | 0.5470          | 0.8044   | 0.8048    | 0.8044 | 0.7995 |
| 0.6447        | 8.0   | 500  | 0.5309          | 0.8083   | 0.8087    | 0.8083 | 0.8025 |
| 0.608         | 8.99  | 562  | 0.4836          | 0.8337   | 0.8323    | 0.8337 | 0.8300 |
| 0.6196        | 10.0  | 625  | 0.4797          | 0.8331   | 0.8293    | 0.8331 | 0.8268 |
| 0.6031        | 10.99 | 687  | 0.4863          | 0.8264   | 0.8274    | 0.8264 | 0.8239 |
| 0.5462        | 12.0  | 750  | 0.4749          | 0.8354   | 0.8341    | 0.8354 | 0.8313 |
| 0.5868        | 12.99 | 812  | 0.5269          | 0.8236   | 0.8268    | 0.8236 | 0.8171 |
| 0.5844        | 14.0  | 875  | 0.4402          | 0.8472   | 0.8447    | 0.8472 | 0.8430 |
| 0.5326        | 14.99 | 937  | 0.4635          | 0.8393   | 0.8359    | 0.8393 | 0.8353 |
| 0.5313        | 16.0  | 1000 | 0.4734          | 0.8365   | 0.8345    | 0.8365 | 0.8300 |
| 0.4893        | 16.99 | 1062 | 0.4675          | 0.8365   | 0.8335    | 0.8365 | 0.8316 |
| 0.4983        | 18.0  | 1125 | 0.4441          | 0.8444   | 0.8431    | 0.8444 | 0.8401 |
| 0.518         | 18.99 | 1187 | 0.4693          | 0.8416   | 0.8441    | 0.8416 | 0.8376 |
| 0.5228        | 20.0  | 1250 | 0.4732          | 0.8410   | 0.8379    | 0.8410 | 0.8358 |
| 0.4761        | 20.99 | 1312 | 0.4567          | 0.8489   | 0.8493    | 0.8489 | 0.8460 |
| 0.5311        | 22.0  | 1375 | 0.4582          | 0.8484   | 0.8469    | 0.8484 | 0.8433 |
| 0.4894        | 22.99 | 1437 | 0.4627          | 0.8467   | 0.8450    | 0.8467 | 0.8433 |
| 0.4791        | 24.0  | 1500 | 0.4580          | 0.8506   | 0.8493    | 0.8506 | 0.8481 |
| 0.479         | 24.99 | 1562 | 0.4625          | 0.8472   | 0.8443    | 0.8472 | 0.8433 |
| 0.4487        | 26.0  | 1625 | 0.4557          | 0.8495   | 0.8469    | 0.8495 | 0.8447 |
| 0.4515        | 26.99 | 1687 | 0.4501          | 0.8534   | 0.8510    | 0.8534 | 0.8500 |
| 0.4862        | 28.0  | 1750 | 0.4552          | 0.8551   | 0.8529    | 0.8551 | 0.8513 |
| 0.4348        | 28.99 | 1812 | 0.4512          | 0.8506   | 0.8486    | 0.8506 | 0.8469 |
| 0.4623        | 29.76 | 1860 | 0.4539          | 0.8551   | 0.8533    | 0.8551 | 0.8516 |


### Framework versions

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