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# UL_interior_classification

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2517
- Accuracy: 0.5876

## 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: 7

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.7547        | 0.9811 | 13   | 2.3422          | 0.3285   |
| 1.7119        | 1.9623 | 26   | 1.8850          | 0.4964   |
| 1.249         | 2.9434 | 39   | 1.5653          | 0.5292   |
| 0.8838        | 4.0    | 53   | 1.3675          | 0.5693   |
| 0.8896        | 4.9811 | 66   | 1.2907          | 0.5803   |
| 0.7262        | 5.9623 | 79   | 1.2625          | 0.5803   |
| 0.6817        | 6.8679 | 91   | 1.2517          | 0.5876   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

bicycle_storage building_interiors cinema_room communal_lounge dining_area entertainment_area fitness_room games_area gym laundry_area living_area living_area_shared_kitchen meeting_room parking reception rooftoop_area storage_lockers study_area swimming_pool