Object Detection
ultralytics
yolo11
computer-vision
aws-architecture
azure-architecture
threat-modeling
stride
Instructions to use guillherms/vision-architecture-analyzer-yolo11-detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use guillherms/vision-architecture-analyzer-yolo11-detect with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("guillherms/vision-architecture-analyzer-yolo11-detect") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Vision Architecture Analyzer YOLO11 Detect - v3.0.0
Overview
This model detects cloud architecture components in AWS/Azure-style diagrams.
It outputs bounding boxes with:
- class id
- class name
- confidence score
Training Details
- Base model: YOLO11s
- Image size: 1280
- Dataset: STRIDE Architecture Components (AWS & Azure)
- Augmentations: disabled geometric transforms to preserve diagram structure
- Run dir:
train2
Artifacts
Version folder: v3.0.0/
Latest folder: latest/
Includes:
best.pt(also uploaded at repo root)last.pt(also uploaded at repo root)- args.yaml (if available)
- results.csv (if available)
- training plots (if available)
- dataset data.yaml
- classes.txt
Usage Example
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict("image.png", imgsz=1280, conf=0.2)
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