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# scene_classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the indoor-scene-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6106
- Accuracy: 0.8492

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.3172        | 1.0   | 341   | 2.8572          | 0.5109   |
| 2.2254        | 2.0   | 682   | 2.1453          | 0.6329   |
| 1.6202        | 3.0   | 1023  | 1.6283          | 0.7336   |
| 1.2313        | 4.0   | 1364  | 1.3402          | 0.7599   |
| 0.9576        | 5.0   | 1705  | 1.1237          | 0.8010   |
| 0.7654        | 6.0   | 2046  | 1.0270          | 0.8023   |
| 0.6416        | 7.0   | 2387  | 0.8848          | 0.8171   |
| 0.5353        | 8.0   | 2728  | 0.8381          | 0.8087   |
| 0.4516        | 9.0   | 3069  | 0.7570          | 0.8254   |
| 0.3925        | 10.0  | 3410  | 0.6667          | 0.8524   |
| 0.3453        | 11.0  | 3751  | 0.7583          | 0.8164   |
| 0.2944        | 12.0  | 4092  | 0.6783          | 0.8350   |
| 0.294         | 13.0  | 4433  | 0.7128          | 0.8312   |
| 0.2507        | 14.0  | 4774  | 0.6632          | 0.8331   |
| 0.2355        | 15.0  | 5115  | 0.6730          | 0.8421   |
| 0.2267        | 16.0  | 5456  | 0.6572          | 0.8357   |
| 0.2032        | 17.0  | 5797  | 0.7058          | 0.8280   |
| 0.1908        | 18.0  | 6138  | 0.6374          | 0.8485   |
| 0.1857        | 19.0  | 6479  | 0.6831          | 0.8312   |
| 0.1727        | 20.0  | 6820  | 0.6961          | 0.8254   |
| 0.1692        | 21.0  | 7161  | 0.6306          | 0.8402   |
| 0.1642        | 22.0  | 7502  | 0.6291          | 0.8485   |
| 0.1618        | 23.0  | 7843  | 0.6058          | 0.8582   |
| 0.1593        | 24.0  | 8184  | 0.6780          | 0.8389   |
| 0.1399        | 25.0  | 8525  | 0.6330          | 0.8485   |
| 0.1373        | 26.0  | 8866  | 0.6550          | 0.8408   |
| 0.1334        | 27.0  | 9207  | 0.6857          | 0.8421   |
| 0.1388        | 28.0  | 9548  | 0.6338          | 0.8415   |
| 0.1423        | 29.0  | 9889  | 0.6272          | 0.8517   |
| 0.1288        | 30.0  | 10230 | 0.6409          | 0.8556   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

meeting_room cloister computerroom grocerystore hospitalroom buffet office warehouse garage bookstore florist locker_room stairscase inside_bus subway fastfood_restaurant auditorium studiomusic airport_inside pantry restaurant_kitchen casino movietheater restaurant kitchen waitingroom artstudio toystore kindergarden trainstation bedroom mall corridor bar hairsalon classroom shoeshop dentaloffice videostore laboratorywet tv_studio church_inside operating_room jewelleryshop bathroom children_room clothingstore closet winecellar livingroom nursery gameroom inside_subway deli bakery library dining_room prisoncell gym concert_hall greenhouse elevator poolinside bowling lobby museum laundromat