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End of preview. Expand in Data Studio
GlassEye 3-Way Unified Building Defect Dataset (640px)
The GlassEye 3-Way Unified Building Defect Dataset is a curated, multi-domain benchmark corpus designed for training computer vision models to detect structural building defects (cracks, spalling, efflorescence) across close-up façade photography and high-altitude aerial drone surveys.
- Primary Repository: GlassEye GitHub
- Model Checkpoint:
sanjeevafk/glasseye-yolo - Image Count: 2,899 total training images + 289 held-out validation images + 200 untouched aerial drone test images
- Resolution: 640px native longest-edge resolution
- Format: Standard YOLO format (
images/+labels/.txtfiles) wrapped inglasseye_3way_dataset_640.zip
Dataset Composition & Source Synthesis
This dataset unifies three complementary civil engineering and building inspection sources to bridge the domain gap between close-up ground photography and high-altitude drone captures:
| Component Dataset | Original Domain | Contributed Images | Key Features Captured |
|---|---|---|---|
| BFDD (Building Façade Defect Dataset) | Close-range building façades | 600 images | Brickwork cracks, plaster weathering, masonry deterioration |
| CUBIT (Concrete Infrastructure) | Civil infrastructure (bridges, abutments) | 699 images | Micro-cracks, concrete spalling, rebar exposure |
| UAV2K (Unmanned Aerial Vehicle 2K) | Distant aerial drone surveys | 1,600 images | High-altitude roof/façade defects, long-range aerial perspectives |
Directory & Label Structure
The primary archive glasseye_3way_dataset_640.zip unpacks into standard Ultralytics YOLO format:
glasseye_3way_dataset_640/
├── data.yaml
├── train/
│ ├── images/ (2,899 .jpg files)
│ └── labels/ (2,899 .txt files)
├── val/
│ ├── images/ (289 .jpg files)
│ └── labels/ (289 .txt files)
└── test/
├── images/ (200 untouched UAV2K drone holdout images)
└── labels/ (200 .txt files)
Class Mapping
- Class ID
0:defect(unified single-class structural anomaly covering cracks, spalling, and weathering)
Bounding Box Format (labels/*.txt)
Standard YOLO normalized coordinates:
<class_id> <x_center> <y_center> <width> <height>
Usage & Quickstart
1. Download & Unpack in Python / Colab
from huggingface_hub import hf_hub_download
import zipfile
# Download dataset zip
zip_path = hf_hub_download(
repo_id="sanjeevafk/glasseye-dataset",
filename="glasseye_3way_dataset_640.zip",
repo_type="dataset"
)
# Extract archive
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall("./data")
2. Fine-Tune YOLO Model
from ultralytics import YOLO
# Load base model
model = YOLO("yolov8n.pt")
# Train on GlassEye dataset
results = model.train(
data="./data/glasseye_3way_dataset_640/data.yaml",
epochs=50,
imgsz=640,
batch=16,
device=0
)
Citation & License
- License: MIT License
- Maintainer: Sanjeev (
sanjeevafk) - Project: GlassEye Autonomous Aerial Drone Inspection System (Physical AI Hackathon 2026)
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