---
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-phones
  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: 0.8653846153846154
---

<!-- 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-phones

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: 0.3938
- Accuracy: 0.8654

## 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: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9333  | 7    | 0.6743          | 0.5673   |
| 0.6763        | 2.0     | 15   | 0.6166          | 0.6923   |
| 0.635         | 2.9333  | 22   | 0.5646          | 0.7404   |
| 0.5724        | 4.0     | 30   | 0.5074          | 0.7308   |
| 0.5724        | 4.9333  | 37   | 0.4809          | 0.7692   |
| 0.527         | 6.0     | 45   | 0.4597          | 0.7692   |
| 0.5304        | 6.9333  | 52   | 0.4758          | 0.7596   |
| 0.4597        | 8.0     | 60   | 0.4343          | 0.7885   |
| 0.4597        | 8.9333  | 67   | 0.4249          | 0.7981   |
| 0.4606        | 10.0    | 75   | 0.4236          | 0.7981   |
| 0.4286        | 10.9333 | 82   | 0.4055          | 0.8462   |
| 0.3857        | 12.0    | 90   | 0.4144          | 0.8269   |
| 0.3857        | 12.9333 | 97   | 0.4294          | 0.7981   |
| 0.3801        | 14.0    | 105  | 0.4081          | 0.8462   |
| 0.3538        | 14.9333 | 112  | 0.4195          | 0.8462   |
| 0.3585        | 16.0    | 120  | 0.4069          | 0.8558   |
| 0.3585        | 16.9333 | 127  | 0.3971          | 0.8558   |
| 0.3258        | 18.0    | 135  | 0.3938          | 0.8654   |
| 0.3288        | 18.9333 | 142  | 0.3964          | 0.8462   |
| 0.3276        | 20.0    | 150  | 0.4423          | 0.8558   |
| 0.3276        | 20.9333 | 157  | 0.4067          | 0.8365   |
| 0.317         | 22.0    | 165  | 0.4179          | 0.8654   |
| 0.288         | 22.9333 | 172  | 0.3882          | 0.8558   |
| 0.2735        | 24.0    | 180  | 0.4215          | 0.8558   |
| 0.2735        | 24.9333 | 187  | 0.3972          | 0.8462   |
| 0.2805        | 26.0    | 195  | 0.3943          | 0.8558   |
| 0.2961        | 26.9333 | 202  | 0.3999          | 0.8558   |
| 0.2832        | 28.0    | 210  | 0.4043          | 0.8558   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1