---
license: apache-2.0
base_model: mansee/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-img_orientation
  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: 0.9661252256004442
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-img_orientation

This model is a fine-tuned version of [mansee/swin-tiny-patch4-window7-224](https://huggingface.co/mansee/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1087
- Accuracy: 0.9661

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5043        | 1.0   | 506  | 0.3764          | 0.8426   |
| 0.3557        | 2.0   | 1013 | 0.2068          | 0.9245   |
| 0.315         | 3.0   | 1519 | 0.1581          | 0.9442   |
| 0.2674        | 4.0   | 2026 | 0.1468          | 0.9490   |
| 0.2615        | 5.0   | 2532 | 0.1245          | 0.9561   |
| 0.2115        | 6.0   | 3039 | 0.1236          | 0.9560   |
| 0.2121        | 7.0   | 3545 | 0.1144          | 0.9624   |
| 0.2053        | 8.0   | 4052 | 0.1143          | 0.9614   |
| 0.1576        | 9.0   | 4558 | 0.1087          | 0.9661   |
| 0.2054        | 9.99  | 5060 | 0.1086          | 0.9657   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3