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

This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5226
- Accuracy: 0.8591
- Precision: 0.8571
- Recall: 0.8591
- F1: 0.8567

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0846        | 0.99  | 62   | 0.8092          | 0.7272   | 0.7246    | 0.7272 | 0.7101 |
| 0.7867        | 2.0   | 125  | 0.6366          | 0.7988   | 0.7996    | 0.7988 | 0.7895 |
| 0.6835        | 2.99  | 187  | 0.5315          | 0.8207   | 0.8195    | 0.8207 | 0.8157 |
| 0.586         | 4.0   | 250  | 0.4611          | 0.8489   | 0.8468    | 0.8489 | 0.8452 |
| 0.5263        | 4.99  | 312  | 0.4753          | 0.8399   | 0.8421    | 0.8399 | 0.8400 |
| 0.5341        | 6.0   | 375  | 0.4551          | 0.8427   | 0.8433    | 0.8427 | 0.8386 |
| 0.4743        | 6.99  | 437  | 0.4639          | 0.8382   | 0.8433    | 0.8382 | 0.8391 |
| 0.4573        | 8.0   | 500  | 0.4771          | 0.8360   | 0.8422    | 0.8360 | 0.8345 |
| 0.4368        | 8.99  | 562  | 0.4731          | 0.8472   | 0.8450    | 0.8472 | 0.8452 |
| 0.4022        | 10.0  | 625  | 0.4736          | 0.8540   | 0.8528    | 0.8540 | 0.8516 |
| 0.4005        | 10.99 | 687  | 0.4542          | 0.8551   | 0.8554    | 0.8551 | 0.8547 |
| 0.3514        | 12.0  | 750  | 0.5543          | 0.8467   | 0.8527    | 0.8467 | 0.8471 |
| 0.3565        | 12.99 | 812  | 0.5318          | 0.8506   | 0.8535    | 0.8506 | 0.8493 |
| 0.3717        | 14.0  | 875  | 0.5059          | 0.8579   | 0.8582    | 0.8579 | 0.8574 |
| 0.3343        | 14.99 | 937  | 0.5235          | 0.8472   | 0.8492    | 0.8472 | 0.8474 |
| 0.3053        | 16.0  | 1000 | 0.5226          | 0.8591   | 0.8571    | 0.8591 | 0.8567 |
| 0.2607        | 16.99 | 1062 | 0.5654          | 0.8591   | 0.8579    | 0.8591 | 0.8572 |
| 0.2814        | 18.0  | 1125 | 0.5622          | 0.8546   | 0.8541    | 0.8546 | 0.8537 |
| 0.2735        | 18.99 | 1187 | 0.6185          | 0.8506   | 0.8525    | 0.8506 | 0.8508 |
| 0.2673        | 20.0  | 1250 | 0.6210          | 0.8574   | 0.8544    | 0.8574 | 0.8550 |
| 0.2595        | 20.99 | 1312 | 0.6334          | 0.8422   | 0.8415    | 0.8422 | 0.8399 |
| 0.2583        | 22.0  | 1375 | 0.6565          | 0.8540   | 0.8545    | 0.8540 | 0.8527 |
| 0.239         | 22.99 | 1437 | 0.6859          | 0.8455   | 0.8458    | 0.8455 | 0.8447 |
| 0.2174        | 24.0  | 1500 | 0.6709          | 0.8591   | 0.8581    | 0.8591 | 0.8581 |
| 0.2288        | 24.99 | 1562 | 0.7437          | 0.8444   | 0.8426    | 0.8444 | 0.8419 |
| 0.2305        | 26.0  | 1625 | 0.7048          | 0.8529   | 0.8497    | 0.8529 | 0.8505 |
| 0.2071        | 26.99 | 1687 | 0.7152          | 0.8540   | 0.8527    | 0.8540 | 0.8529 |
| 0.2282        | 28.0  | 1750 | 0.7273          | 0.8568   | 0.8559    | 0.8568 | 0.8554 |
| 0.209         | 28.99 | 1812 | 0.7213          | 0.8557   | 0.8534    | 0.8557 | 0.8540 |
| 0.2078        | 29.76 | 1860 | 0.7273          | 0.8563   | 0.8544    | 0.8563 | 0.8548 |


### Framework versions

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