shutingmi/aic_project4_building_elements

This is a indoor building element dataset with 37 classes.

This dataset consists of 900 original images collected from the ETH building videos collected by Aria glasses, Excel sheet: 230116_Ernest-Bloch_Inventaire and web-scraped samples from gruner-reuse.ch using the script parse_grunerweb.ipynb. The dataset is randomly split into 720 training, 90 validation, and 90 test images.

After applying data augmentations, the final dataset contains a total of 2,340 images, including:

• 2,160 augmented training samples
• 90 validation samples
• 90 test samples

Preprocessing

• Auto-Orient: Applied
• Resize: Stretched to 640×640
• Auto-Adjust Contrast: Using Adaptive Equalization

Augmentations

Each training example is augmented to generate 3 output variations with the following transformations:

• Hue: Between −15° and +15°
• Saturation: Between −25% and +25%
• Brightness: Between −15% and +15%
• Blur: Up to 1px
• Noise: Up to 0.1% of pixels

Coco format:

 ```
data_new_coco/
├── train/
│   ├── images/
│   └── annotations/
├── valid/
│   ├── images/
│   └── annotations/
├── test/
│   ├── images/
│   └── annotations/
├── README.dataset.txt
└── README.roboflow.txt 
``` 

Yolo format:

 ```
data_new_yolo/
├── train/
│   ├── images/
│   └── labels/
├── valid/
│   ├── images/
│   └── labels/
├── test/
│   ├── images/
│   └── labels/
├── data.yaml
├── README.dataset.txt
└── README.roboflow.txt
```