# swin-tiny-patch4-window7-224-shortSleeveCleanedData

**Repository Path**: modelee/swin-tiny-patch4-window7-224-shortSleeveCleanedData

## Basic Information

- **Project Name**: swin-tiny-patch4-window7-224-shortSleeveCleanedData
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 4
- **Forks**: 0
- **Created**: 2023-05-23
- **Last Updated**: 2025-09-02

## Categories & Tags

**Categories**: llm

**Tags**: None

## README

---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-shortSleeveCleanedData
  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: 0.994535519125683
---

<!-- 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-shortSleeveCleanedData

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: 0.0355
- Accuracy: 0.9945

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1819        | 1.0   | 147  | 0.0471          | 0.9880   |
| 0.1431        | 2.0   | 294  | 0.0457          | 0.9891   |
| 0.1001        | 3.0   | 441  | 0.0392          | 0.9891   |
| 0.116         | 4.0   | 588  | 0.0451          | 0.9880   |
| 0.1144        | 5.0   | 735  | 0.0398          | 0.9902   |
| 0.0787        | 6.0   | 882  | 0.0441          | 0.9902   |
| 0.0998        | 7.0   | 1029 | 0.0320          | 0.9902   |
| 0.124         | 8.0   | 1176 | 0.0364          | 0.9902   |
| 0.103         | 9.0   | 1323 | 0.0395          | 0.9880   |
| 0.0591        | 10.0  | 1470 | 0.0299          | 0.9913   |
| 0.0445        | 11.0  | 1617 | 0.0302          | 0.9913   |
| 0.0684        | 12.0  | 1764 | 0.0350          | 0.9880   |
| 0.0358        | 13.0  | 1911 | 0.0408          | 0.9891   |
| 0.0548        | 14.0  | 2058 | 0.0382          | 0.9902   |
| 0.0611        | 15.0  | 2205 | 0.0331          | 0.9923   |
| 0.0231        | 16.0  | 2352 | 0.0355          | 0.9945   |
| 0.046         | 17.0  | 2499 | 0.0321          | 0.9934   |
| 0.0648        | 18.0  | 2646 | 0.0327          | 0.9923   |
| 0.0565        | 19.0  | 2793 | 0.0320          | 0.9923   |
| 0.0413        | 20.0  | 2940 | 0.0327          | 0.9923   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3