---
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-wuhan
  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: 0.2222222222222222
---

<!-- 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-wuhan

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: 6.1948
- Accuracy: 0.2222

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 3    | 0.7953          | 0.4      |
| No log        | 2.0   | 6    | 0.9477          | 0.4      |
| No log        | 3.0   | 9    | 1.0106          | 0.4      |
| 0.5883        | 4.0   | 12   | 1.4170          | 0.4      |
| 0.5883        | 5.0   | 15   | 1.7436          | 0.4      |
| 0.5883        | 6.0   | 18   | 2.5380          | 0.4      |
| 0.241         | 7.0   | 21   | 3.8803          | 0.4      |
| 0.241         | 8.0   | 24   | 2.4040          | 0.2222   |
| 0.241         | 9.0   | 27   | 3.9968          | 0.4      |
| 0.125         | 10.0  | 30   | 3.2731          | 0.4      |
| 0.125         | 11.0  | 33   | 3.2202          | 0.2222   |
| 0.125         | 12.0  | 36   | 4.7008          | 0.4      |
| 0.125         | 13.0  | 39   | 4.5588          | 0.3556   |
| 0.0766        | 14.0  | 42   | 4.5434          | 0.2444   |
| 0.0766        | 15.0  | 45   | 4.9792          | 0.2667   |
| 0.0766        | 16.0  | 48   | 5.4095          | 0.2667   |
| 0.0239        | 17.0  | 51   | 5.8507          | 0.2222   |
| 0.0239        | 18.0  | 54   | 6.1023          | 0.2222   |
| 0.0239        | 19.0  | 57   | 6.1666          | 0.2222   |
| 0.0129        | 20.0  | 60   | 6.1948          | 0.2222   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3