metadata
license: other
license_name: bsl-1.1
license_link: https://mariadb.com/bsl11/
library_name: ultralytics
tags:
- onnx
- yolo
- yolov8
- object-detection
- whiteboard
- diagram
- shapes
pipeline_tag: object-detection
Whiteboard Detector
Detects hand-drawn shapes on whiteboards.
YOLOv8-nano fine-tuned to recognize 30 diagram shape classes.
Quick Stats
| Spec | Value |
|---|---|
| Architecture | YOLOv8-nano |
| Format | ONNX |
| Size | ~12 MB |
| Input | 640×640 RGB |
| Classes | 30 |
| Training | 100 epochs, 211 images |
| Hardware | M3 Max, 1.4 hours |
Classes (30)
rectangle, rounded_rectangle, oval, circle, diamond, hexagon,
parallelogram, triangle, star, cloud, cylinder, stick_figure,
arrow_box, document_shape, database_icon, square, ellipse,
pentagon, cross, heart, lightning, banner, callout, bracket,
solid_arrow, dashed_arrow, bidirectional_arrow, dotted_line,
curved_arrow, curved_line
Usage
Python (ultralytics)
from ultralytics import YOLO
model = YOLO("best.onnx")
results = model("whiteboard.jpg")
for box in results[0].boxes:
cls = int(box.cls[0])
conf = float(box.conf[0])
x1, y1, x2, y2 = box.xyxy[0].tolist()
print(f"{model.names[cls]}: {conf:.2f} at ({x1:.0f}, {y1:.0f})")
Python (onnxruntime)
import onnxruntime as ort
import numpy as np
from PIL import Image
# Load model
session = ort.InferenceSession("best.onnx")
# Preprocess
img = Image.open("whiteboard.jpg").resize((640, 640))
input_tensor = np.array(img).transpose(2, 0, 1).astype(np.float32) / 255.0
input_tensor = input_tensor[np.newaxis, ...]
# Inference
outputs = session.run(None, {"images": input_tensor})
# outputs[0] shape: [1, 34, 8400]
# 34 = 4 (xywh) + 30 (class scores)
# 8400 = detection candidates
CLI (ultralytics)
yolo predict model=best.onnx source=whiteboard.jpg
Output Format
YOLO outputs tensor [1, 34, 8400]:
For each of 8400 candidates:
[0] x_center (0-640)
[1] y_center (0-640)
[2] width
[3] height
[4-33] confidence per class (30 classes)
Post-process with confidence threshold (0.25) and NMS (0.45 IoU).
Training Performance
| Class | mAP50 | Notes |
|---|---|---|
| cloud | 0.993 | Excellent |
| rounded_rectangle | 0.995 | Excellent |
| stick_figure | 0.895 | Good |
| oval | 0.849 | Good |
| rectangle | 0.716 | Good |
| text_label | 0.664 | Fair |
| solid_arrow | 0.368 | Needs more data |
| triangle | 0.316 | Needs more data |
| cylinder | 0.045 | Needs more data |
Files
whiteboard-detector/
├── best.onnx # Model (use this)
├── best.pt # PyTorch weights
├── classes.txt # Class names
├── README.md # This file
└── SKILL.md # Manifest
Training Data
- 211 annotated whiteboard images
- Hand-drawn diagrams, varying styles
- Augmentation: rotation, blur, noise
Limitations
- Best with clear contrast (dark ink on white)
- Small shapes (<20px) may be missed
- Overlapping shapes can confuse detection
- Some classes undertrained (cylinder, triangle)
License
Business Source License 1.1 (BSL-1.1)
Copyright (c) 2024 Block Xaero Inc.
- ✅ Free for non-production use
- ⚠️ Production use requires license