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# architectural_styles_classifier

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Architectural styles dataset, retrieved from https://www.kaggle.com/datasets/dumitrux/architectural-styles-dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9412
- Accuracy: 0.7223

## Model description

Presentation about the model: https://www.canva.com/design/DAGLBMAs1K4/d8qvLN2nchSYVmnrwYzx0w/edit?utm_content=DAGLBMAs1K4&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton

You can try the model from Huggingface Space this link: https://huggingface.co/spaces/hanslab37/technospire

## Intended uses & limitations

The model were developed as part of experiment to learn about training a model and developing Image Classification model with Gradio in Huggingface. You can use it for experiment only. Not recommended for daily use.

## Training and evaluation data

https://www.kaggle.com/datasets/dumitrux/architectural-styles-dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0003
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.8855        | 0.9960  | 110  | 1.7753          | 0.4457   |
| 1.583         | 1.9921  | 220  | 1.6208          | 0.4829   |
| 1.4343        | 2.9972  | 331  | 1.3291          | 0.5851   |
| 1.2836        | 3.9932  | 441  | 1.2550          | 0.6005   |
| 1.2885        | 4.9983  | 552  | 1.1483          | 0.6298   |
| 1.1226        | 5.9943  | 662  | 1.1245          | 0.6491   |
| 0.985         | 6.9994  | 773  | 1.1381          | 0.6397   |
| 0.9963        | 7.9955  | 883  | 1.0964          | 0.6605   |
| 0.88          | 8.9915  | 993  | 1.0407          | 0.6739   |
| 0.7688        | 9.9966  | 1104 | 1.0288          | 0.6918   |
| 0.763         | 10.9926 | 1214 | 0.9835          | 0.6898   |
| 0.6287        | 11.9977 | 1325 | 1.0049          | 0.7037   |
| 0.6229        | 12.9938 | 1435 | 1.1010          | 0.6918   |
| 0.5731        | 13.9989 | 1546 | 0.9910          | 0.7082   |
| 0.5076        | 14.9949 | 1656 | 1.0457          | 0.7112   |
| 0.554         | 16.0    | 1767 | 1.0141          | 0.7007   |
| 0.382         | 16.9960 | 1877 | 1.0606          | 0.6928   |
| 0.459         | 17.9921 | 1987 | 1.0091          | 0.7161   |
| 0.4018        | 18.9972 | 2098 | 1.0011          | 0.7072   |
| 0.3981        | 19.9208 | 2200 | 0.9821          | 0.7310   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1

achaemenid architecture american foursquare architecture chicago school architecture colonial architecture deconstructivism edwardian architecture georgian architecture gothic architecture greek revival architecture international style novelty architecture palladian architecture american craftsman style postmodern architecture queen anne architecture romanesque architecture russian revival architecture tudor revival architecture ancient egyptian architecture art deco architecture art nouveau architecture baroque architecture bauhaus architecture beaux-arts architecture byzantine architecture