---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S2_M1_R3_swint_42507052
  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: 0.996607294317218
---

<!-- 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. -->

# S2_M1_R3_swint_42507052

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0177
- Accuracy: 0.9966

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0359        | 0.99  | 73   | 0.0208          | 0.9949   |
| 0.0209        | 1.99  | 147  | 0.0115          | 0.9975   |
| 0.0088        | 3.0   | 221  | 0.0281          | 0.9958   |
| 0.0029        | 4.0   | 295  | 0.0185          | 0.9958   |
| 0.0002        | 4.95  | 365  | 0.0177          | 0.9966   |


### Framework versions

- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0