---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-DMAE-U3
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.45652173913043476
---

<!-- 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-DMAE-U3

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7136
- Accuracy: 0.4565

## 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: 5.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.86  | 3    | 7.9328          | 0.1087   |
| No log        | 2.0   | 7    | 7.7596          | 0.1087   |
| 7.8559        | 2.86  | 10   | 7.3241          | 0.1087   |
| 7.8559        | 4.0   | 14   | 6.4450          | 0.1087   |
| 7.8559        | 4.86  | 17   | 5.7723          | 0.1087   |
| 6.3363        | 6.0   | 21   | 4.8772          | 0.1087   |
| 6.3363        | 6.86  | 24   | 4.2678          | 0.1087   |
| 6.3363        | 8.0   | 28   | 3.5000          | 0.1087   |
| 4.1887        | 8.86  | 31   | 2.9766          | 0.1087   |
| 4.1887        | 10.0  | 35   | 2.3876          | 0.1087   |
| 4.1887        | 10.86 | 38   | 2.0429          | 0.1087   |
| 2.602         | 12.0  | 42   | 1.7136          | 0.4565   |
| 2.602         | 12.86 | 45   | 1.5478          | 0.4565   |
| 2.602         | 14.0  | 49   | 1.3874          | 0.4565   |
| 1.6353        | 14.86 | 52   | 1.2968          | 0.4565   |
| 1.6353        | 16.0  | 56   | 1.2225          | 0.4565   |
| 1.6353        | 16.86 | 59   | 1.2071          | 0.4565   |
| 1.2533        | 18.0  | 63   | 1.2177          | 0.3261   |
| 1.2533        | 18.86 | 66   | 1.2197          | 0.3261   |
| 1.2088        | 20.0  | 70   | 1.2112          | 0.4565   |
| 1.2088        | 20.86 | 73   | 1.2101          | 0.4565   |
| 1.2088        | 22.0  | 77   | 1.2092          | 0.4565   |
| 1.1798        | 22.86 | 80   | 1.2081          | 0.4565   |
| 1.1798        | 24.0  | 84   | 1.2076          | 0.4565   |
| 1.1798        | 24.86 | 87   | 1.2049          | 0.4565   |
| 1.1825        | 26.0  | 91   | 1.2045          | 0.4565   |
| 1.1825        | 26.86 | 94   | 1.2029          | 0.4565   |
| 1.1825        | 28.0  | 98   | 1.2022          | 0.4565   |
| 1.1943        | 28.86 | 101  | 1.2014          | 0.4565   |
| 1.1943        | 30.0  | 105  | 1.2040          | 0.4565   |
| 1.1943        | 30.86 | 108  | 1.2050          | 0.4565   |
| 1.1772        | 32.0  | 112  | 1.2031          | 0.4565   |
| 1.1772        | 32.86 | 115  | 1.2019          | 0.4565   |
| 1.1772        | 34.0  | 119  | 1.2013          | 0.4565   |
| 1.1945        | 34.29 | 120  | 1.2012          | 0.4565   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0