metadata
license: apache-2.0
metrics:
- precision
base_model:
- Ultralytics/YOLO11s
tags:
- object_detection
model-index:
- name: Object Detection Model
results:
- task:
type: object-detection
dataset:
name: Custom Object Dataset
type: object-detection
metrics:
- name: Box(P)
type: precision
value: 0.904
- name: R
type: recall
value: 0.87
- name: mAP50
type: mAP
value: 0.918
- name: mAP50-95
type: mAP
value: 0.671
details:
- class:
name: all
images: 92
instances: 2568
metrics:
- Box(P): 0.904
- R: 0.87
- mAP50: 0.918
- mAP50-95: 0.671
- class:
name: range
images: 82
instances: 82
metrics:
- Box(P): 0.928
- R: 0.938
- mAP50: 0.957
- mAP50-95: 0.701
- class:
name: entry_door
images: 92
instances: 821
metrics:
- Box(P): 0.941
- R: 0.944
- mAP50: 0.966
- mAP50-95: 0.704
- class:
name: kitchen_sink
images: 80
instances: 91
metrics:
- Box(P): 0.863
- R: 0.828
- mAP50: 0.917
- mAP50-95: 0.662
- class:
name: bathroom_sink
images: 89
instances: 240
metrics:
- Box(P): 0.909
- R: 0.85
- mAP50: 0.929
- mAP50-95: 0.64
- class:
name: toilet
images: 90
instances: 188
metrics:
- Box(P): 0.927
- R: 0.904
- mAP50: 0.96
- mAP50-95: 0.667
- class:
name: double_folding_door
images: 19
instances: 37
metrics:
- Box(P): 0.867
- R: 0.702
- mAP50: 0.828
- mAP50-95: 0.594
- class:
name: window
images: 88
instances: 669
metrics:
- Box(P): 0.871
- R: 0.9
- mAP50: 0.905
- mAP50-95: 0.582
- class:
name: shower
images: 61
instances: 70
metrics:
- Box(P): 0.907
- R: 0.957
- mAP50: 0.947
- mAP50-95: 0.778
- class:
name: bathtub
images: 71
instances: 103
metrics:
- Box(P): 0.947
- R: 0.874
- mAP50: 0.933
- mAP50-95: 0.793
- class:
name: single_folding_door
images: 55
instances: 144
metrics:
- Box(P): 0.877
- R: 0.839
- mAP50: 0.9
- mAP50-95: 0.647
- class:
name: dishwasher
images: 49
instances: 54
metrics:
- Box(P): 0.912
- R: 0.833
- mAP50: 0.863
- mAP50-95: 0.568
- class:
name: refrigerator
images: 66
instances: 69
metrics:
- Box(P): 0.901
- R: 0.87
- mAP50: 0.916
- mAP50-95: 0.712
source:
name: Custom Object Detection Results
url: https://example.com/custom-object-detection-results
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.