---
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-15e
  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: 0.10869565217391304
---

<!-- 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-15e

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: 7.9238
- 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: 1.5e-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
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.86  | 3    | 7.9238          | 0.1087   |
| No log        | 2.0   | 7    | 7.8746          | 0.1087   |
| 7.9273        | 2.86  | 10   | 7.8185          | 0.1087   |
| 7.9273        | 4.0   | 14   | 7.6996          | 0.1087   |
| 7.9273        | 4.86  | 17   | 7.5876          | 0.1087   |
| 7.6529        | 6.0   | 21   | 7.4189          | 0.1087   |
| 7.6529        | 6.86  | 24   | 7.2678          | 0.1087   |
| 7.6529        | 8.0   | 28   | 7.0489          | 0.1087   |
| 7.1057        | 8.86  | 31   | 6.8846          | 0.1087   |
| 7.1057        | 10.0  | 35   | 6.6868          | 0.1087   |
| 7.1057        | 10.86 | 38   | 6.5595          | 0.1087   |
| 6.8483        | 12.0  | 42   | 6.3826          | 0.1087   |
| 6.8483        | 12.86 | 45   | 6.2276          | 0.1087   |
| 6.8483        | 14.0  | 49   | 6.0366          | 0.1087   |
| 6.224         | 14.86 | 52   | 5.9044          | 0.1087   |
| 6.224         | 16.0  | 56   | 5.7383          | 0.1087   |
| 6.224         | 16.86 | 59   | 5.6266          | 0.1087   |
| 5.8234        | 18.0  | 63   | 5.4871          | 0.1087   |
| 5.8234        | 18.86 | 66   | 5.3891          | 0.1087   |
| 5.5423        | 20.0  | 70   | 5.2672          | 0.1087   |
| 5.5423        | 20.86 | 73   | 5.1809          | 0.1087   |
| 5.5423        | 22.0  | 77   | 5.0741          | 0.1087   |
| 5.2547        | 22.86 | 80   | 5.0007          | 0.1087   |
| 5.2547        | 24.0  | 84   | 4.9116          | 0.1087   |
| 5.2547        | 24.86 | 87   | 4.8505          | 0.1087   |
| 5.0166        | 26.0  | 91   | 4.7770          | 0.1087   |
| 5.0166        | 26.86 | 94   | 4.7281          | 0.1087   |
| 5.0166        | 28.0  | 98   | 4.6712          | 0.1087   |
| 4.7751        | 28.86 | 101  | 4.6348          | 0.1087   |
| 4.7751        | 30.0  | 105  | 4.5943          | 0.1087   |
| 4.7751        | 30.86 | 108  | 4.5701          | 0.1087   |
| 4.7321        | 32.0  | 112  | 4.5458          | 0.1087   |
| 4.7321        | 32.86 | 115  | 4.5336          | 0.1087   |
| 4.7321        | 34.0  | 119  | 4.5255          | 0.1087   |
| 4.6731        | 34.29 | 120  | 4.5249          | 0.1087   |


### Framework versions

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