---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-base-patch4-window8-256
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7241379310344828
---

<!-- 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-base-patch4-window8-256

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6211
- Accuracy: 0.7241

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5428        | 0.9912  | 28   | 0.6211          | 0.7241   |
| 0.6494        | 1.9823  | 56   | 0.6130          | 0.7241   |
| 0.5752        | 2.9735  | 84   | 0.6846          | 0.7241   |
| 0.7165        | 4.0     | 113  | 0.9642          | 0.7241   |
| 0.5699        | 4.9912  | 141  | 0.6072          | 0.7241   |
| 0.5517        | 5.9823  | 169  | 0.6231          | 0.7241   |
| 0.5268        | 6.9735  | 197  | 0.6098          | 0.7241   |
| 0.672         | 8.0     | 226  | 0.5891          | 0.7241   |
| 0.5448        | 8.9912  | 254  | 0.6023          | 0.7241   |
| 0.555         | 9.9823  | 282  | 0.5917          | 0.7241   |
| 0.5818        | 10.9735 | 310  | 0.5940          | 0.7241   |
| 0.6556        | 12.0    | 339  | 0.5966          | 0.7241   |
| 0.716         | 12.9912 | 367  | 0.5904          | 0.7241   |
| 0.6104        | 13.9823 | 395  | 0.5938          | 0.7241   |
| 0.5046        | 14.9735 | 423  | 0.5921          | 0.7241   |
| 0.5871        | 16.0    | 452  | 0.6027          | 0.7241   |
| 0.5222        | 16.9912 | 480  | 0.5921          | 0.7241   |
| 0.5511        | 17.9823 | 508  | 0.5948          | 0.7241   |
| 0.6394        | 18.9735 | 536  | 0.5969          | 0.7241   |
| 0.566         | 20.0    | 565  | 0.6005          | 0.7241   |
| 0.6032        | 20.9912 | 593  | 0.5968          | 0.7241   |
| 0.4824        | 21.9823 | 621  | 0.5934          | 0.7241   |
| 0.4975        | 22.9735 | 649  | 0.5979          | 0.7241   |
| 0.4976        | 24.0    | 678  | 0.6034          | 0.7241   |
| 0.5355        | 24.9912 | 706  | 0.6033          | 0.7241   |
| 0.4323        | 25.9823 | 734  | 0.6015          | 0.7241   |
| 0.5579        | 26.9735 | 762  | 0.6043          | 0.7241   |
| 0.5639        | 28.0    | 791  | 0.6023          | 0.7241   |
| 0.5595        | 28.9912 | 819  | 0.5996          | 0.7241   |
| 0.4372        | 29.7345 | 840  | 0.5995          | 0.7241   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1