Specialized Solution

Custom Model Training

Bespoke computer vision and OCR model training engineered specifically for non-standard architectural drawings, blueprints, and CAD renders.

Key Extraction Targets

Fine-Tuned YOLO Weights
Custom Legend Classes
OCR Synthetic Fine-Tuning
Line & Hatch Detectors
Anchor Optimization
Confidence Calibration

The Problem

Pre-trained models struggle with domain-specific blueprint graphics.

Models trained on COCO or generic web imagery fail when trying to detect CAD line work, architectural hatch patterns, and complex drawing callouts.

System Workflow

How the AI System Works

01

Data Curation & Synthetic Augmentation

Annotate floor plan features and generate synthetic variations (rotations, scales, noise) to expand training dataset size.

02

Hyperparameter Tuning & Training

Train state-of-the-art vision architectures (YOLOv8/v10, RT-DETR) with custom anchor boxes tuned to CAD geometries.

03

Benchmark Evaluation & Quantization

Benchmark against test sets (mAP50-95, IoU, Precision/Recall) and quantize for fast CPU/GPU inference.

Capabilities

Engineered Features

Architectural Dataset Annotation
Synthetic Blueprint Data Augmentation
Custom Anchor & Aspect Ratio Tuning
TensorRT / ONNX Model Quantization
Confidence Threshold Calibration
Model Versioning & MLOps Pipelines

Architecture

Technical Stack

PyTorch 2.x & CUDA 12READY
Ultralytics & HuggingFace TransformersREADY
ONNX Runtime & TensorRT 10READY
Weights & Biases Experiment TrackingREADY
Docker Containerized InferenceREADY

Relevant Case Study

Custom Door & Schedule Extraction Model

Trained a custom object detection model achieving 96.8% mAP on non-standard CAD door symbols.

Read Case Study

Frequently Asked Questions

A complete dataset curation, training, and benchmarking cycle typically takes 2 to 4 weeks.

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