---
license: apache-2.0
base_model: mansee/swin-tiny-patch4-window7-224-blank_img
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-blank_img
  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: 0.9738372093023255
---

<!-- 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-blank_img

This model is a fine-tuned version of [mansee/swin-tiny-patch4-window7-224-blank_img](https://huggingface.co./mansee/swin-tiny-patch4-window7-224-blank_img) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1016
- Accuracy: 0.9738

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0502        | 0.99  | 72   | 0.1300          | 0.9651   |
| 0.1107        | 1.99  | 145  | 0.1023          | 0.9729   |
| 0.0917        | 3.0   | 218  | 0.1277          | 0.9651   |
| 0.1022        | 4.0   | 291  | 0.1258          | 0.9719   |
| 0.0888        | 4.95  | 360  | 0.1016          | 0.9738   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0