---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-vit0
  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: 0.8314176245210728
---

<!-- 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-vit0

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4836
- Accuracy: 0.8314

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.13          | 0.97  | 18   | 1.0297          | 0.4330   |
| 0.9066        | 2.0   | 37   | 0.8349          | 0.6590   |
| 0.7157        | 2.97  | 55   | 0.8050          | 0.6743   |
| 0.6446        | 4.0   | 74   | 0.6934          | 0.7165   |
| 0.5707        | 4.97  | 92   | 0.6324          | 0.7433   |
| 0.5042        | 6.0   | 111  | 0.6156          | 0.7356   |
| 0.4714        | 6.97  | 129  | 0.6825          | 0.7241   |
| 0.4225        | 8.0   | 148  | 0.5692          | 0.7625   |
| 0.3912        | 8.97  | 166  | 0.6150          | 0.7586   |
| 0.3442        | 10.0  | 185  | 0.4901          | 0.8008   |
| 0.289         | 10.97 | 203  | 0.5580          | 0.7739   |
| 0.2827        | 12.0  | 222  | 0.5308          | 0.7969   |
| 0.2375        | 12.97 | 240  | 0.5274          | 0.8046   |
| 0.2493        | 14.0  | 259  | 0.5433          | 0.8046   |
| 0.2309        | 14.97 | 277  | 0.5355          | 0.7931   |
| 0.1963        | 16.0  | 296  | 0.4836          | 0.8314   |
| 0.2162        | 16.97 | 314  | 0.4973          | 0.8238   |
| 0.2256        | 18.0  | 333  | 0.4918          | 0.8276   |
| 0.2124        | 18.97 | 351  | 0.5071          | 0.8161   |
| 0.1797        | 19.46 | 360  | 0.4985          | 0.8199   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0