---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12-192-22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12-192-22k-huggingface
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9230769230769231
---

<!-- 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-large-patch4-window12-192-22k-huggingface

This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6329
- Accuracy: 0.9231

## 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: 0.0001
- train_batch_size: 18
- eval_batch_size: 18
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 36
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.781         | 0.97  | 16   | 0.7972          | 0.7385   |
| 0.5567        | 2.0   | 33   | 0.4750          | 0.8308   |
| 0.3648        | 2.97  | 49   | 0.5832          | 0.8308   |
| 0.4016        | 4.0   | 66   | 0.5669          | 0.8      |
| 0.2448        | 4.97  | 82   | 0.5883          | 0.8462   |
| 0.3382        | 6.0   | 99   | 0.8846          | 0.8      |
| 0.3042        | 6.97  | 115  | 0.5364          | 0.8769   |
| 0.2744        | 8.0   | 132  | 0.5159          | 0.8769   |
| 0.1859        | 8.97  | 148  | 0.5541          | 0.8462   |
| 0.1787        | 10.0  | 165  | 0.4850          | 0.8923   |
| 0.181         | 10.97 | 181  | 0.4529          | 0.9077   |
| 0.113         | 12.0  | 198  | 0.7836          | 0.8154   |
| 0.0806        | 12.97 | 214  | 0.7141          | 0.8769   |
| 0.0929        | 14.0  | 231  | 0.5765          | 0.9231   |
| 0.1208        | 14.97 | 247  | 0.5762          | 0.9231   |
| 0.0764        | 16.0  | 264  | 0.6146          | 0.9231   |
| 0.099         | 16.97 | 280  | 0.5736          | 0.9231   |
| 0.0972        | 18.0  | 297  | 0.6051          | 0.9231   |
| 0.0534        | 18.97 | 313  | 0.6302          | 0.9231   |
| 0.0754        | 19.39 | 320  | 0.6329          | 0.9231   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1