---
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-RH
  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: 0.6448598130841121
---

<!-- 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-RH

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: 0.6681
- Accuracy: 0.6449

## 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: 0.00015
- 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        | 1.0   | 8    | 4.5659          | 0.4112   |
| 4.5175        | 2.0   | 16   | 3.6362          | 0.4112   |
| 3.9284        | 3.0   | 24   | 1.6019          | 0.4112   |
| 1.6086        | 4.0   | 32   | 0.7110          | 0.4112   |
| 0.7392        | 5.0   | 40   | 0.6825          | 0.5888   |
| 0.7392        | 6.0   | 48   | 0.6795          | 0.5888   |
| 0.7073        | 7.0   | 56   | 0.6814          | 0.5888   |
| 0.6956        | 8.0   | 64   | 0.7061          | 0.5888   |
| 0.6898        | 9.0   | 72   | 0.7014          | 0.5888   |
| 0.7026        | 10.0  | 80   | 0.7214          | 0.4112   |
| 0.7026        | 11.0  | 88   | 0.7186          | 0.5888   |
| 0.7696        | 12.0  | 96   | 0.6837          | 0.5888   |
| 0.6909        | 13.0  | 104  | 0.6823          | 0.5888   |
| 0.6799        | 14.0  | 112  | 0.6781          | 0.5888   |
| 0.6782        | 15.0  | 120  | 0.6938          | 0.5888   |
| 0.6782        | 16.0  | 128  | 0.6766          | 0.5888   |
| 0.6952        | 17.0  | 136  | 0.7123          | 0.5888   |
| 0.6875        | 18.0  | 144  | 0.6891          | 0.5607   |
| 0.6919        | 19.0  | 152  | 0.7076          | 0.5888   |
| 0.6751        | 20.0  | 160  | 0.7011          | 0.4953   |
| 0.6751        | 21.0  | 168  | 0.6962          | 0.5888   |
| 0.689         | 22.0  | 176  | 0.6857          | 0.5701   |
| 0.6826        | 23.0  | 184  | 0.6935          | 0.5888   |
| 0.6841        | 24.0  | 192  | 0.7219          | 0.5888   |
| 0.6657        | 25.0  | 200  | 0.6610          | 0.5888   |
| 0.6657        | 26.0  | 208  | 0.6681          | 0.6449   |
| 0.6524        | 27.0  | 216  | 0.7225          | 0.5888   |
| 0.6567        | 28.0  | 224  | 0.7117          | 0.5888   |
| 0.6402        | 29.0  | 232  | 0.6999          | 0.6262   |
| 0.66          | 30.0  | 240  | 0.6799          | 0.6075   |
| 0.66          | 31.0  | 248  | 0.6677          | 0.6075   |
| 0.6469        | 32.0  | 256  | 0.6735          | 0.5981   |
| 0.6355        | 33.0  | 264  | 0.6853          | 0.6168   |
| 0.6245        | 34.0  | 272  | 0.7008          | 0.6262   |
| 0.6306        | 35.0  | 280  | 0.6990          | 0.5981   |
| 0.6306        | 36.0  | 288  | 0.6981          | 0.6355   |
| 0.6208        | 37.0  | 296  | 0.7103          | 0.6262   |
| 0.6339        | 38.0  | 304  | 0.7050          | 0.6355   |
| 0.5959        | 39.0  | 312  | 0.6989          | 0.6355   |
| 0.6059        | 40.0  | 320  | 0.6990          | 0.6355   |


### Framework versions

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