blockxaero/cyan-sketch

Cyan Sketch - Whiteboard Shape Detector

YOLOv8n model for detecting shapes and connectors in whiteboard/flowchart images.

Model Details

• Architecture: YOLOv8n (nano)
• Format: ONNX
• Input Size: 640x640
• Classes: 30 shape types

Performance

Metric	Value
mAP50	0.592
mAP50-95	0.339

Per-Class Performance (Top 10)

Class	mAP50
rounded_rectangle	0.995
stick_figure	0.995
cloud	0.980
rectangle	0.857
sticky_note	0.857
cylinder	0.823
text_label	0.774
circle	0.738
oval	0.735
diamond	0.713

Classes (30)

rectangle, rounded_rectangle, oval, circle, diamond, triangle,
cylinder, cloud, hexagon, parallelogram, sticky_note, stick_figure,
solid_arrow, dashed_arrow, bidirectional_arrow, line, curved_arrow,
start_dot, end_dot, text_label, ellipse, square,
curved_bidirectional_arrow, dashed_line, dotted_line, dotted_arrow,
solid_circle, double_solid_line, dashed_oval, curved_line

Usage

import onnxruntime as ort
import cv2
import numpy as np

# Load model
session = ort.InferenceSession("best.onnx")

# Load classes
with open("classes.txt") as f:
    classes = [l.strip() for l in f]

# Preprocess image
img = cv2.imread("whiteboard.jpg")
resized = cv2.resize(img, (640, 640))
blob = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
blob = np.transpose(blob, (2, 0, 1))[None, ...]

# Run inference
outputs = session.run(None, {"images": blob})[0]

# Parse detections (conf > 0.3)
for i in range(outputs.shape[2]):
    scores = outputs[0, 4:, i]
    class_id = np.argmax(scores)
    conf = scores[class_id]
    if conf > 0.3:
        print(f"{classes[class_id]}: {conf:.2f}")

Files

• best.onnx - ONNX model (6MB)
• classes.txt - Class names
• ocr_dictionary.json - Domain terms for OCR correction

License

Business Source License - See LICENSE file