---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-finetuned-ve-Ub200
  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: 0.47058823529411764
---

<!-- 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-finetuned-ve-Ub200

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: 1.5977
- Accuracy: 0.4706

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.92  | 6    | 7.9891          | 0.0980   |
| No log        | 2.0   | 13   | 7.4848          | 0.0980   |
| No log        | 2.92  | 19   | 6.2378          | 0.0980   |
| No log        | 4.0   | 26   | 4.8900          | 0.0980   |
| No log        | 4.92  | 32   | 3.8155          | 0.0980   |
| No log        | 6.0   | 39   | 2.7342          | 0.0980   |
| No log        | 6.92  | 45   | 2.0612          | 0.0980   |
| No log        | 8.0   | 52   | 1.5977          | 0.4706   |
| No log        | 8.92  | 58   | 1.3671          | 0.4706   |
| No log        | 10.0  | 65   | 1.2122          | 0.4706   |
| No log        | 10.92 | 71   | 1.1823          | 0.4706   |
| No log        | 12.0  | 78   | 1.1835          | 0.4706   |
| No log        | 12.92 | 84   | 1.1838          | 0.4706   |
| No log        | 14.0  | 91   | 1.1778          | 0.4706   |
| No log        | 14.92 | 97   | 1.1769          | 0.4706   |
| 3.2267        | 16.0  | 104  | 1.1762          | 0.4706   |
| 3.2267        | 16.92 | 110  | 1.1758          | 0.4706   |
| 3.2267        | 18.0  | 117  | 1.1770          | 0.4706   |
| 3.2267        | 18.46 | 120  | 1.1771          | 0.4706   |


### Framework versions

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