GlassEye YOLOv8n (BFDD + CUBIT Combined Checkpoint)
glasseye-yolo-bfdd-cubit-v1 is a fine-tuned YOLOv8n model built for autonomous building façade defect detection within the GlassEye inspection pipeline.
It is trained on a combined dataset of real-world façade defect imagery from BFDD (Building Façade Defect Dataset) and CUBIT (Concrete Ultrasound / Building Inspection dataset), unified under a single binary defect class.
Model Summary
- Base Architecture: YOLOv8n (
yolov8n.pt, 3.2M parameters) - Framework: PyTorch (
torch 2.13+cu130) / Ultralytics8.4.120 - Class Map:
0: defect(binary classification for façade surface and structural defects) - Input Resolution: 320 × 320 px
- Model Checkpoint Size: 6.2 MB (
best.pt) - SHA-256:
df78a4f90c776d8f122ec02972a193f91b1f4fc3916f1d78a4f51bb2e05445cf
Benchmark & Performance
Evaluated strictly across three separate test sets using standard IoU thresholds (IoU = 0.50, confidence = 0.20, imgsz = 320):
1. BFDD Held-Out Test Benchmark (Primary Ground-Truth)
149 untouched images, 1,033 mask-derived ground truth boxes.
| Model Checkpoint | mAP@50 | mAP@50-95 | Recall |
|---|---|---|---|
Synthetic Baseline (glasseye-yolo-v1) | 0.0131 | 0.0099 | 0.0165 |
BFDD-only (glasseye-yolo-real-bfdd-v1) | 0.0940 | 0.0429 | 0.1413 |
BFDD + CUBIT (glasseye-yolo-bfdd-cubit-v1) | 0.1512 | 0.0707 | 0.2014 |
Improvement: BFDD+CUBIT achieves +60.8% mAP@50, +64.8% mAP@50-95, and +42.5% Recall over the BFDD-only model on untouched held-out building façade images.
2. CUBIT Test Benchmark
701 images, 5,085 derived polygon boxes.
| Model Checkpoint | Full Test mAP@50 | Full Test Recall | Far-Subset mAP@50 (≥10 frames dist) |
|---|---|---|---|
| Synthetic Baseline | 0.0031 | 0.0069 | 0.0000 |
| BFDD-only | 0.0211 | 0.0350 | 0.0164 |
| BFDD + CUBIT | 0.1990 | 0.2061 | 0.1279 |
3. UAV2K Test Benchmark (Aerial Out-of-Domain)
200 images, 527 derived boxes (high-resolution aerial drone captures).
| Model Checkpoint | mAP@50 | Recall | True Positives |
|---|---|---|---|
| Synthetic Baseline | 0.0000 | 0.0000 | 0 |
| BFDD-only | 0.0000 | 0.0000 | 0 |
| BFDD + CUBIT | 0.0132 | 0.0133 | 7 |
Training Details
- Dataset:
data/bfdd_cubit_binary_v1(Train: 1,299 images [600 BFDD + 699 CUBIT val-as-train]; Val: 89 BFDD images; Test: 850 images). - Optimizer: AdamW
- Epochs: 32
- Batch Size: 4
- Image Size: 320 px
- Seed:
20260815(deterministic) - Augmentations: Horizontal flip (
0.5), HSV-H (0.01), HSV-S (0.2), HSV-V (0.15), Mosaic (0.0).
Quickstart & Usage
1. Installation
pip install ultralytics torch huggingface_hub
2. Run Inference in Python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# Download model from Hugging Face
model_path = hf_hub_download(
repo_id="sanjeevafk/glasseye-yolo-bfdd-cubit-v1",
filename="best.pt"
)
# Load model
model = YOLO(model_path)
# Predict on an image or video
results = model.predict(
source="path/to/facade_image.jpg",
conf=0.20,
iou=0.45,
imgsz=320,
save=True
)
for r in results:
for box in r.boxes:
print(f"Detected {model.names[int(box.cls)]} at {box.xyxy.tolist()} (conf: {float(box.conf):.3f})")
3. Ultralytics CLI
# Run detection directly
yolo predict model=best.pt source="https://example.com/facade.jpg" conf=0.20 imgsz=320
Project Context
This model is integrated into GlassEye, a deterministic façade-inspection pipeline featuring YOLO object detection, panel projection mapping, event logging, and advisory VLM second opinions.
- Live Application: glasseye-td75.onrender.com
- Source Repository: github.com/sanjeevafk/glasseye