Fast CPU-only ROI Detection Library for Real-Time Pipelines 🚀
IDet is a fast, production-oriented CPU-only C++ library for image detection pipelines, built on top of ONNX Runtime. Library supports three modes: text detection (DBNet / DBNet++ / PP-OCR-style models), face detection (SCRFD family) and cloth detection (YOLO family). Key features include tiled inference, polygon NMS, IOBinding (zero per-frame allocations), explicit threading and memory control, and reproducible performance profiles for modern multi-core CPUs.
Most demo repos optimize for “it runs”. IDet optimizes for:
- CPU-first deployment
- reproducible performance experiments
- low allocation churn
- controllable threading
- maintainable C++ integration
- model-agnostic detector pipelines (within supported output contracts)
This makes IDet suitable for:
- server-side CPU inference
- embedded-ish x86 / ARM deployments (when GPU is not available or not desired)
- benchmarking and systems-level performance tuning
- integrating detection into larger C++ products
Detailed documentation is organized into focused, self-contained pages under docs/:
| Page | What it covers |
|---|---|
| Requirements | Host dependencies for Linux/macOS and project scope |
| Build & Install | Toolchain profiles, ORT setup, Meson options, build/test/install flow |
| Model Zoo | Supported model families, conversion notes, compatibility rules |
| Command-line Options | Actual idet_app flags, defaults, validation rules |
| Quick Start | Text / face / cloth scripts and direct smoke commands |
| C++ Integration Guide | Blocking API, hot-loop worker, image lifetime, fixed-shape IOBinding |
| Performance Guide | Runtime policy, benchmark output, tuning, IOBinding, tiling and NMS |
| Troubleshooting & FAQ | Common failures, ORT ABI mismatch, FAQ |
| Doxygen API Reference | Generate and browse local API HTML documentation |
source toolchain/activate.sh
scripts/build.sh force -- -Didet_libtype=shared
scripts/run_tests.sh
scripts/run_idet_text.shBuild the checked C++ integration examples:
scripts/build.sh force -- -Dbuild_examples=trueThen run, from repository root:
"${BUILD_DIR}/examples/sync_detector"
"${BUILD_DIR}/examples/hot_loop_worker"sync_detector runs one image and prints every detected quad. hot_loop_worker reads
assets/videos/test.yuv as I420 video by default and processes frames asynchronously while the
application loop continues its own work.
- ⚡ High-performance CPU inference (x86 / ARM, Linux & MacOS)
- 🧠 Multiple detection pipelines: text, face, and cloth detection
- 🧩 Tiled inference (
RxC) with overlap for small-object recall - 📐 Polygon-based post-processing with NMS
- 💾 ONNX Runtime IOBinding for reusable input/output buffers
- 📈 Benchmark mode with p50 / p90 / p95 / p99 latency
- 🔒 Accurate logging & error handling: all interaction goes through wrappers
- 🧵 Explicit threading model:
- OpenMP → outer parallelism (tiles / batches)
- ONNX Runtime → intra-op / inter-op graph execution
- 🔧 Runtime policy controls:
- affinity / topology-aware execution (where supported)
- optional NUMA memory locality helpers
- OpenCV thread suppression to avoid oversubscription
IDet is intentionally a low-level inference toolkit, not a full OCR stack or face recognition framework.
- ONNX Runtime-based CPU detector execution
- pre-processing / post-processing for supported detector families
- tiled inference and result stitching
- performance benchmarking and runtime policy control
- library integration + CLI demo tooling
- OCR text recognition and language models
- ROI recognition / embeddings / tracking
- dataset labeling / training pipelines
- GUI applications
This project uses such libraries / frameworks:
- OpenCV (image data processing, isolated behind adapters in library internals)
- OpenMP (fast tiled inference)
- ONNX Runtime (inference core engine)
- NUMA (cpu/mem binding topology for multi-socket nodes)
- GTest (test coverage)
- Indicators (pretty output with progress bar)
Supported model families:
- DBNet / DBNet++ / PP-OCR (text detection)
- SCRFD (face detection)
- YOLO (cloth detection)