---
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-8e-6
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.10869565217391304
---

<!-- 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-8e-6

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: 6.3664
- Accuracy: 0.1087

## 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: 8e-06
- 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: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.86  | 3    | 7.9427          | 0.1087   |
| No log        | 2.0   | 7    | 7.9381          | 0.1087   |
| 7.9636        | 2.86  | 10   | 7.9301          | 0.1087   |
| 7.9636        | 4.0   | 14   | 7.9088          | 0.1087   |
| 7.9636        | 4.86  | 17   | 7.8857          | 0.1087   |
| 7.8732        | 6.0   | 21   | 7.8450          | 0.1087   |
| 7.8732        | 6.86  | 24   | 7.8049          | 0.1087   |
| 7.8732        | 8.0   | 28   | 7.7376          | 0.1087   |
| 7.6568        | 8.86  | 31   | 7.6783          | 0.1087   |
| 7.6568        | 10.0  | 35   | 7.5943          | 0.1087   |
| 7.6568        | 10.86 | 38   | 7.5288          | 0.1087   |
| 7.7458        | 12.0  | 42   | 7.4353          | 0.1087   |
| 7.7458        | 12.86 | 45   | 7.3610          | 0.1087   |
| 7.7458        | 14.0  | 49   | 7.2614          | 0.1087   |
| 7.3025        | 14.86 | 52   | 7.1894          | 0.1087   |
| 7.3025        | 16.0  | 56   | 7.0993          | 0.1087   |
| 7.3025        | 16.86 | 59   | 7.0348          | 0.1087   |
| 7.0862        | 18.0  | 63   | 6.9525          | 0.1087   |
| 7.0862        | 18.86 | 66   | 6.8945          | 0.1087   |
| 6.9553        | 20.0  | 70   | 6.8253          | 0.1087   |
| 6.9553        | 20.86 | 73   | 6.7795          | 0.1087   |
| 6.9553        | 22.0  | 77   | 6.7202          | 0.1087   |
| 6.8024        | 22.86 | 80   | 6.6757          | 0.1087   |
| 6.8024        | 24.0  | 84   | 6.6210          | 0.1087   |
| 6.8024        | 24.86 | 87   | 6.5785          | 0.1087   |
| 6.6652        | 26.0  | 91   | 6.5275          | 0.1087   |
| 6.6652        | 26.86 | 94   | 6.4949          | 0.1087   |
| 6.6652        | 28.0  | 98   | 6.4589          | 0.1087   |
| 6.467         | 28.86 | 101  | 6.4354          | 0.1087   |
| 6.467         | 30.0  | 105  | 6.4094          | 0.1087   |
| 6.467         | 30.86 | 108  | 6.3946          | 0.1087   |
| 6.4984        | 32.0  | 112  | 6.3796          | 0.1087   |
| 6.4984        | 32.86 | 115  | 6.3719          | 0.1087   |
| 6.4984        | 34.0  | 119  | 6.3668          | 0.1087   |
| 6.4603        | 34.29 | 120  | 6.3664          | 0.1087   |


### Framework versions

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