YOLO11s Auto-CAD Detection Model Card

Model Overview

The YOLO11s Auto-CAD detection model is a computer vision model trained to detect various objects related to AutoCAD layouts, such as kitchen sinks, toilets, windows, and other fixtures. This model is based on the YOLO11s architecture and fine-tuned for Auto-CAD-specific object detection tasks. The model is optimized for real-time inference on both GPU and CPU platforms.

Version Information

Model Version: YOLO11
Ultralytics Version: 8.3.8
Python Version: 3.9.7
Torch Version: 2.3.1+cu118
CUDA Version: 11.8 (for GPU use)
Hardware: NVIDIA GeForce RTX 4060 Laptop GPU, AMD Ryzen 7 7745HX (for CPU evaluation)
Model Architecture: YOLO (You Only Look Once) v5 (fused)

Model Details

Layers: 238
Parameters: 9,417,444
GFLOPs: 21.3
Training Type: Supervised learning
Target Platform: GPUs (NVIDIA RTX series) and CPUs (AMD Ryzen)

Supported Tasks

Object detection on AutoCAD layouts
Classification and localization of various CAD objects

Dataset

The model was trained on a dataset of AutoCAD object instances, including:

Range
Entry Door
Kitchen Sink
Bathroom Sink
Toilet
Double Folding Door
Window
Shower
Bathtub
Single Folding Door
Dishwasher
Refrigerator

Each object class has a varying number of images and instances in the training set.

Performance Metrics

Class	Precision	Recall	mAP50	mAP50-95
all	0.904	0.87	0.918	0.671
range	0.928	0.938	0.957	0.701
entry_door	0.941	0.944	0.966	0.704
kitchen_sink	0.863	0.828	0.917	0.662
bathroom_sink	0.909	0.85	0.929	0.64
toilet	0.927	0.904	0.96	0.667
double_folding_door	0.867	0.702	0.828	0.594
window	0.871	0.9	0.905	0.582
shower	0.907	0.957	0.947	0.778
bathtub	0.947	0.874	0.933	0.793
single_folding_door	0.877	0.839	0.9	0.647
dishwasher	0.912	0.833	0.863	0.568
refrigerator	0.901	0.87	0.916	0.712

Inference Speed

Preprocess Time: 0.2ms per image
Inference Time: 13.4ms per image
Postprocess Time: 1.5ms per image

These times may vary depending on the hardware platform and the number of objects in the input image.

Usage

Requirements

Python 3.9.7
PyTorch 2.3.1+cu118
CUDA-enabled GPU (optional but recommended for faster inference)
Ultralytics YOLO package 8.3.8

Installation

To use the model, install the necessary dependencies:

pip install torch==2.3.1+cu118
pip install ultralytics

Demo

You can try the model in action through this interactive demo:
YOLO11 Auto-CAD Detection Demo

Notes

The model supports various object categories in AutoCAD drawings, such as doors, sinks, bathtubs, etc.
Performance metrics like mAP50 and mAP50-95 indicate the accuracy of detection and classification across multiple object categories.
This model is optimized for both GPU and CPU, with higher performance on GPUs. The model can be used for real-time detection applications requiring accurate localization of AutoCAD objects.

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HouseCADDesignObjectDetection