---
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-U
  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: 0.6521739130434783
---

<!-- 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-U

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: 1.0462
- Accuracy: 0.6522

## 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: 4e-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    | 1.3654          | 0.4783   |
| No log        | 2.0   | 7    | 1.3027          | 0.4565   |
| 1.3356        | 2.86  | 10   | 1.2580          | 0.4565   |
| 1.3356        | 4.0   | 14   | 1.2157          | 0.4565   |
| 1.3356        | 4.86  | 17   | 1.2121          | 0.4565   |
| 1.202         | 6.0   | 21   | 1.2014          | 0.4565   |
| 1.202         | 6.86  | 24   | 1.2013          | 0.4565   |
| 1.202         | 8.0   | 28   | 1.1949          | 0.4565   |
| 1.1884        | 8.86  | 31   | 1.1934          | 0.4565   |
| 1.1884        | 10.0  | 35   | 1.1916          | 0.4565   |
| 1.1884        | 10.86 | 38   | 1.1829          | 0.4565   |
| 1.1351        | 12.0  | 42   | 1.1568          | 0.4565   |
| 1.1351        | 12.86 | 45   | 1.1371          | 0.4565   |
| 1.1351        | 14.0  | 49   | 1.1238          | 0.4783   |
| 1.132         | 14.86 | 52   | 1.1183          | 0.5217   |
| 1.132         | 16.0  | 56   | 1.0962          | 0.6087   |
| 1.132         | 16.86 | 59   | 1.0737          | 0.6087   |
| 1.0659        | 18.0  | 63   | 1.0462          | 0.6522   |
| 1.0659        | 18.86 | 66   | 1.0217          | 0.6304   |
| 1.0299        | 20.0  | 70   | 0.9955          | 0.6522   |
| 1.0299        | 20.86 | 73   | 0.9767          | 0.6304   |
| 1.0299        | 22.0  | 77   | 0.9495          | 0.6304   |
| 0.9684        | 22.86 | 80   | 0.9328          | 0.6304   |
| 0.9684        | 24.0  | 84   | 0.9176          | 0.6304   |
| 0.9684        | 24.86 | 87   | 0.9078          | 0.6304   |
| 0.9301        | 26.0  | 91   | 0.8966          | 0.6304   |
| 0.9301        | 26.86 | 94   | 0.8951          | 0.6304   |
| 0.9301        | 28.0  | 98   | 0.8894          | 0.6522   |
| 0.9258        | 28.86 | 101  | 0.8820          | 0.6304   |
| 0.9258        | 30.0  | 105  | 0.8771          | 0.6304   |
| 0.9258        | 30.86 | 108  | 0.8776          | 0.6522   |
| 0.8877        | 32.0  | 112  | 0.8754          | 0.6522   |
| 0.8877        | 32.86 | 115  | 0.8732          | 0.6522   |
| 0.8877        | 34.0  | 119  | 0.8721          | 0.6522   |
| 0.8953        | 34.29 | 120  | 0.8719          | 0.6522   |


### Framework versions

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