---
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-ve-Ub
  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: 0.09803921568627451
---

<!-- 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-ve-Ub

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: 53618207116050790111890112512.0000
- Accuracy: 0.0980

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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                             | 0.57  | 1    | 53618207116050790111890112512.0000 | 0.0980   |
| No log                             | 1.71  | 3    | 53618207116050790111890112512.0000 | 0.0980   |
| No log                             | 2.86  | 5    | 53618207116050790111890112512.0000 | 0.0980   |
| No log                             | 4.0   | 7    | 53618207116050790111890112512.0000 | 0.0980   |
| No log                             | 4.57  | 8    | 53618207116050790111890112512.0000 | 0.0980   |
| 52775460204830951003775827968.0000 | 5.71  | 10   | 53618207116050790111890112512.0000 | 0.0980   |
| 52775460204830951003775827968.0000 | 6.86  | 12   | 53618207116050790111890112512.0000 | 0.0980   |
| 52775460204830951003775827968.0000 | 8.0   | 14   | 53618207116050790111890112512.0000 | 0.0980   |
| 52775460204830951003775827968.0000 | 8.57  | 15   | 53618207116050790111890112512.0000 | 0.0980   |
| 52775460204830951003775827968.0000 | 9.71  | 17   | 53618207116050790111890112512.0000 | 0.0980   |
| 52775460204830951003775827968.0000 | 10.86 | 19   | 53618207116050790111890112512.0000 | 0.0980   |
| 52792343609480503040906625024.0000 | 12.0  | 21   | 53618207116050790111890112512.0000 | 0.0980   |
| 52792343609480503040906625024.0000 | 12.57 | 22   | 53618207116050790111890112512.0000 | 0.0980   |
| 52792343609480503040906625024.0000 | 13.71 | 24   | 53618207116050790111890112512.0000 | 0.0980   |
| 52792343609480503040906625024.0000 | 14.86 | 26   | 53618207116050790111890112512.0000 | 0.0980   |
| 52792343609480503040906625024.0000 | 16.0  | 28   | 53618207116050790111890112512.0000 | 0.0980   |
| 52792343609480503040906625024.0000 | 16.57 | 29   | 53618207116050790111890112512.0000 | 0.0980   |
| 52872559615505121741583155200.0000 | 17.71 | 31   | 53618207116050790111890112512.0000 | 0.0980   |
| 52872559615505121741583155200.0000 | 18.86 | 33   | 53618207116050790111890112512.0000 | 0.0980   |
| 52872559615505121741583155200.0000 | 20.0  | 35   | 53618207116050790111890112512.0000 | 0.0980   |
| 52872559615505121741583155200.0000 | 20.57 | 36   | 53618207116050790111890112512.0000 | 0.0980   |
| 52872559615505121741583155200.0000 | 21.71 | 38   | 53618207116050790111890112512.0000 | 0.0980   |
| 52783892462422761283050799104.0000 | 22.86 | 40   | 53618207116050790111890112512.0000 | 0.0980   |


### Framework versions

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