---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SW2-DMAE-DA
  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: 0.6304347826086957
---

<!-- 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. -->

# SW2-DMAE-DA

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: 0.8856
- Accuracy: 0.6304

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 6.464         | 0.96  | 11   | 7.9190          | 0.1087   |
| 6.5496        | 2.0   | 23   | 7.5824          | 0.1087   |
| 6.1541        | 2.96  | 34   | 6.4156          | 0.1087   |
| 5.2649        | 4.0   | 46   | 4.5067          | 0.1087   |
| 4.1175        | 4.96  | 57   | 2.7241          | 0.1087   |
| 2.8424        | 6.0   | 69   | 1.5623          | 0.1087   |
| 1.4376        | 6.96  | 80   | 1.4003          | 0.1087   |
| 1.4054        | 8.0   | 92   | 1.4055          | 0.1087   |
| 1.3798        | 8.96  | 103  | 1.3466          | 0.4565   |
| 1.3331        | 10.0  | 115  | 1.4385          | 0.1522   |
| 1.2736        | 10.96 | 126  | 1.3106          | 0.2391   |
| 1.2127        | 12.0  | 138  | 1.2908          | 0.1957   |
| 1.2531        | 12.96 | 149  | 1.2545          | 0.5      |
| 1.0972        | 14.0  | 161  | 1.2515          | 0.3478   |
| 1.0029        | 14.96 | 172  | 1.2238          | 0.2609   |
| 1.0141        | 16.0  | 184  | 1.2067          | 0.3696   |
| 0.9129        | 16.96 | 195  | 1.1149          | 0.5652   |
| 0.9157        | 18.0  | 207  | 1.1957          | 0.3913   |
| 0.8516        | 18.96 | 218  | 1.0034          | 0.5435   |
| 0.7804        | 20.0  | 230  | 0.9991          | 0.4783   |
| 0.7328        | 20.96 | 241  | 0.9840          | 0.5870   |
| 0.7101        | 22.0  | 253  | 0.9661          | 0.5435   |
| 0.7099        | 22.96 | 264  | 0.9392          | 0.5435   |
| 0.7238        | 24.0  | 276  | 0.9553          | 0.5      |
| 0.6605        | 24.96 | 287  | 0.9571          | 0.5435   |
| 0.639         | 26.0  | 299  | 1.0534          | 0.5652   |
| 0.6123        | 26.96 | 310  | 0.9152          | 0.6087   |
| 0.6021        | 28.0  | 322  | 0.8704          | 0.5870   |
| 0.5971        | 28.96 | 333  | 0.8726          | 0.5652   |
| 0.5413        | 30.0  | 345  | 0.8287          | 0.5870   |
| 0.5663        | 30.96 | 356  | 0.9271          | 0.5435   |
| 0.5343        | 32.0  | 368  | 0.8856          | 0.6304   |
| 0.525         | 32.96 | 379  | 0.8579          | 0.6087   |
| 0.5447        | 34.0  | 391  | 0.8746          | 0.5870   |
| 0.5036        | 34.96 | 402  | 0.8684          | 0.5652   |
| 0.4918        | 36.0  | 414  | 0.8268          | 0.5870   |
| 0.503         | 36.96 | 425  | 0.8374          | 0.5870   |
| 0.5114        | 38.0  | 437  | 0.8380          | 0.6087   |
| 0.5272        | 38.26 | 440  | 0.8387          | 0.6087   |


### Framework versions

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