---
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-finetuned-200k
  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: 0.796086508753862
---

<!-- 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-finetuned-200k

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: 0.4347
- Accuracy: 0.7961

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.634         | 0.99  | 36   | 0.6243          | 0.6262   |
| 0.5551        | 1.99  | 72   | 0.5186          | 0.7250   |
| 0.5183        | 2.98  | 108  | 0.4826          | 0.7673   |
| 0.4854        | 4.0   | 145  | 0.5640          | 0.7261   |
| 0.4645        | 4.99  | 181  | 0.4598          | 0.7817   |
| 0.4655        | 5.99  | 217  | 0.4787          | 0.7786   |
| 0.4582        | 6.98  | 253  | 0.4483          | 0.7899   |
| 0.4415        | 8.0   | 290  | 0.4709          | 0.7765   |
| 0.4546        | 8.99  | 326  | 0.4717          | 0.7817   |
| 0.4566        | 9.99  | 362  | 0.4538          | 0.7951   |
| 0.4675        | 10.98 | 398  | 0.4491          | 0.7817   |
| 0.4449        | 12.0  | 435  | 0.4992          | 0.7652   |
| 0.4349        | 12.99 | 471  | 0.4627          | 0.7817   |
| 0.4253        | 13.99 | 507  | 0.4492          | 0.7858   |
| 0.4278        | 14.98 | 543  | 0.4442          | 0.7951   |
| 0.4567        | 16.0  | 580  | 0.4362          | 0.7899   |
| 0.4205        | 16.99 | 616  | 0.4550          | 0.7889   |
| 0.4233        | 17.99 | 652  | 0.4336          | 0.7909   |
| 0.4014        | 18.98 | 688  | 0.4565          | 0.7889   |
| 0.4176        | 20.0  | 725  | 0.4323          | 0.7940   |
| 0.411         | 20.99 | 761  | 0.4348          | 0.7951   |
| 0.4128        | 21.99 | 797  | 0.4378          | 0.7971   |
| 0.4045        | 22.98 | 833  | 0.4317          | 0.7951   |
| 0.4001        | 24.0  | 870  | 0.4452          | 0.7868   |
| 0.4061        | 24.99 | 906  | 0.4286          | 0.7920   |
| 0.4033        | 25.99 | 942  | 0.4306          | 0.7951   |
| 0.3953        | 26.98 | 978  | 0.4320          | 0.7920   |
| 0.3924        | 28.0  | 1015 | 0.4338          | 0.7940   |
| 0.4056        | 28.99 | 1051 | 0.4329          | 0.7930   |
| 0.4032        | 29.79 | 1080 | 0.4347          | 0.7961   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3