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It's organised as a small reusable detection library with\ntwo front-ends on top:\n\n- 🌐 a **REST API** (Axum) that returns detections as JSON, and\n- 🖼️ an **`annotate` CLI** that draws the detected boxes onto an image.\n\n## Demo\n\n\u003cimg src=\"docs/assets/demo.jpg\" alt=\"Detection demo — a street scene with a car, a tram and pedestrians boxed and labelled\" width=\"560\"\u003e\n\nBoxes and labels above were drawn by the bundled `annotate` tool from the\nservice's own detections. Regenerate it (or annotate any image) with:\n\n```bash\ncargo run --release --features draw --bin annotate -- path/to/image.jpg out.jpg\n# or reproduce the demo above:\n./scripts/demo.sh\n```\n\n\u003csub\u003eSample photo: [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Crossing_marked_with_a_traffic_sign_20190517_184744_HDR.jpg) (CC0).\u003c/sub\u003e\n\n## Quick start\n\n```bash\n# 1. download the model (~11 MB) into models/\n./scripts/get_model.sh\n\n# 2. build (the first build also downloads \u0026 statically links ONNX Runtime)\ncargo build --release\n\n# 3. run\n./target/release/cv-inference\n# listening on http://0.0.0.0:8080\n```\n\nIn another terminal:\n\n```bash\ncurl -F \"file=@cat.jpg\" http://localhost:8080/detect\n```\n\n```json\n{\n  \"objects\": [\n    {\n      \"class\": \"person\",\n      \"confidence\": 0.97,\n      \"bbox\": { \"x\": 124.0, \"y\": 80.0, \"width\": 200.0, \"height\": 450.0 }\n    }\n  ]\n}\n```\n\n## Documentation\n\n- [Architecture](docs/architecture.md) — design, project layout, inference pipeline, logging\n- [API reference](docs/api.md) — endpoints, request/response formats, errors\n- [Configuration](docs/configuration.md) — model download, `config.yaml`, environment variables\n- [Deployment](docs/deployment.md) — local build \u0026 run, Docker\n- [Roadmap](docs/roadmap.md) — planned / future improvements\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxmgorin%2Fcv-inference","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmxmgorin%2Fcv-inference","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxmgorin%2Fcv-inference/lists"}