transformers safetensors swin image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swin-tiny-patch4-window7-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: attraction-classifier-swin
  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: 0.739010989010989
---

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# attraction-classifier-swin

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: 0.5367
- Accuracy: 0.7390

## 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: 69
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6207        | 0.49  | 100  | 0.5599          | 0.7115   |
| 0.6256        | 0.98  | 200  | 0.5238          | 0.7225   |
| 0.597         | 1.46  | 300  | 0.5003          | 0.7418   |
| 0.6121        | 1.95  | 400  | 0.5409          | 0.7610   |
| 0.5457        | 2.44  | 500  | 0.5123          | 0.7555   |
| 0.5258        | 2.93  | 600  | 0.4792          | 0.7637   |
| 0.504         | 3.41  | 700  | 0.5169          | 0.7390   |
| 0.541         | 3.9   | 800  | 0.4858          | 0.7582   |
| 0.5704        | 4.39  | 900  | 0.5367          | 0.7390   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
