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Supervised object detector from the ARGUS project (FIAP IADT, Phase 5 Hackathon). It locates cloud/software components in architecture diagram images and classifies them into 21 cloud-agnostic canonical classes (AWS,...
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Supervised object detector from the ARGUS project (FIAP IADT, Phase 5 Hackathon). It locates cloud/software components in architecture diagram images and classifies them into 21 cloud-agnostic canonical classes (AWS, Azure and GCP icons map to the same class). This is stage E1 of the ARGUS pipeline; the later stages (topology, DFD, STRIDE-per-element, Graph-RAG, scoring/report) operate only on the canonical classes, so only this visual stage is coupled to each cloud's iconography.
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Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
actor_user (ExternalEntity)edge_security (Process)api_gateway (Process)load_balancer (Process)compute (Process)serverless_fn (Process)app_service (Process)database_sql (DataStore)database_nosql (DataStore)cache (DataStore)object_storage (DataStore)file_storage (DataStore)message_queue (DataStore)cdn (Process)identity (Process)secrets (DataStore)search (DataStore)monitoring (DataStore)email_notify (Process)backend_external (ExternalEntity)trust_boundary (TrustBoundary)These figures are computed on a held-out split of the synthetic dataset (in-distribution). On real reference diagrams the detector recognizes most components correctly but exhibits a synthetic-to-real gap (e.g., it may confuse load balancers or external web services with the user class, or a key-vault with a database). Closing this gap with a real annotated set is planned future work.
Self-labeled synthetic dataset: official AWS/Azure/GCP architecture icons composited
onto varied backgrounds with arrows, text labels and trust boundaries. Because the icon
positions are known, YOLO labels are emitted automatically (no manual annotation), which
makes the set scalable. Base model: yolo11s, imgsz=1280.
from ultralytics import YOLO
model = YOLO("best.pt")
results = model("diagram.png", conf=0.25, imgsz=1280)
for b in results[0].boxes:
print(results[0].names[int(b.cls[0])], float(b.conf[0]))
--- license: apache-2.0 library_name: ultralytics pipeline_tag: object-detection tags: - object-detection - yolo11 - ultralytics - threat-modeling - architecture-diagrams - stride - security --- # ARGUS — Architecture Component Detector (YOLO11) Supervised object detector from the **ARGUS** project (FIAP IADT, Phase 5 Hackathon). It locates cloud/software components in **architecture diagram images** and classifies them into **21 cloud-agnostic canonical classes** (AWS, Azure and GCP icons map to the same class). This is **stage E1** of the ARGUS pipeline; the later stages (topology, DFD, STRIDE-per-element, Graph-RAG, scoring/report) operate only on the canonical classes, so only this visual stage is coupled to each cloud's iconography. ## Classes (21) - `actor_user` (ExternalEntity) - `edge_security` (Process) - `api_gateway` (Process) - `load_balancer` (Process) - `compute` (Process) - `serverless_fn` (Process) - `app_service` (Process) - `database_sql` (DataStore) - `database_nosql` (DataStore) - `cache` (DataStore) - `object_storage` (DataStore) - `file_storage` (DataStore) - `message_queue` (DataStore) - `cdn` (Process) - `identity` (Process) - `secrets` (DataStore) - `search` (DataStore) - `monitoring` (DataStore) - `email_notify` (Process) - `backend_external` (ExternalEntity) - `trust_boundary` (TrustBoundary) ## Metrics (synthetic test set) - **mAP@50**: 0.9931 - **mAP@50-95**: 0.9858 - **Precision**: 0.9878 - **Recall**: 0.9918 > These figures are computed on a held-out split of the **synthetic** dataset > (in-distribution). On real reference diagrams the detector recognizes most components > correctly but exhibits a **synthetic-to-real gap** (e.g., it may confuse load balancers > or external web services with the user class, or a key-vault with a database). Closing > this gap with a real annotated set is planned future work. ## Training data **Self-labeled synthetic dataset**: official AWS/Azure/GCP architecture icons composited onto varied backgrounds with arrows, text labels and trust boundaries. Because the icon positions are known, YOLO labels are emitted automatically (no manual annotation), which makes the set scalable. Base model: `yolo11s`, `imgsz=1280`. ## Usage ```python from ultralytics import YOLO model = YOLO("best.pt") results = model("diagram.png", conf=0.25, imgsz=1280) for b in results[0].boxes: print(results[0...
Source context: 4 downloads · 1 likes · Pipeline object-detection · Library ultralytics · Repo zagari/argus-detector