yolo-detection-2026

name yolo-detection-2026 description YOLO 2026 — state-of-the-art real-time object detection version 2.0.0 icon assets/icon.png entry scripts/detect.py deploy deploy.sh requirements {"python":">=3.9","ultralytics":">=8.3.0","torch":">=2.4.0","platforms":["linux","macos","windows"]} parameters [{"name":"auto_start","label":"Auto Start","type":"boolean","default":false,"description":"Start this skill automatically when Aegis launches","group":"Lifecycle"},{"name":"model_size","label":"Model Size","type":"select","options":["nano","small","medium","large"],"default":"nano","description":"Larger models are more accurate but slower","group":"Model"},{"name":"confidence","label":"Confidence Threshold","type":"number","min":0.1,"max":1,"default":0.8,"group":"Model"},{"name":"classes","label":"Detect Classes","type":"string","default":"person,car,dog,cat","description":"Comma-separated COCO class names (80 classes available)","group":"Model"},{"name":"fps","label":"Processing FPS","type":"select","options":[0.2,0.5,1,3,5,15],"default":5,"description":"Frames per second — higher = more CPU/GPU usage","group":"Performance"},{"name":"device","label":"Inference Device","type":"select","options":["auto","cpu","cuda","mps","rocm"],"default":"auto","description":"auto = best available GPU, else CPU","group":"Performance"},{"name":"use_optimized","label":"Hardware Acceleration","type":"boolean","default":true,"description":"Auto-convert model to optimized format for faster inference","group":"Performance"},{"name":"compute_units","label":"Apple Compute Units","type":"select","options":["auto","cpu_and_ne","all","cpu_only","cpu_and_gpu"],"default":"auto","description":"CoreML compute target — 'auto' routes to Neural Engine (NPU), leaving GPU free for LLM/VLM","group":"Performance","platform":"macos"}] capabilities {"live_detection":{"script":"scripts/detect.py","description":"Real-time object detection on live camera frames"}} YOLO 2026 Object Detection Real-time object detection using the latest YOLO 2026 models. Detects 80+ COCO object classes including people, vehicles, animals, and everyday objects. Outputs bounding boxes with labels and confidence scores. Model Sizes Size Speed Accuracy Best For nano Fastest Good Real-time on CPU, edge devices small Fast Better Balanced speed/accuracy medium Moderate High Accuracy-focused deployments large Slower Highest Maximum detection quality Hardware Acceleration The skill uses env_config.py to automatically detect hardware and convert the model to the fastest format for your platform. Conversion happens once during deployment and is cached. Platform Backend Optimized Format Compute Units Expected Speedup NVIDIA GPU CUDA TensorRT .engine GPU ~3-5x Apple Silicon (M1+) MPS CoreML .mlpackage Neural Engine (NPU) ~2x Intel CPU/GPU/NPU OpenVINO OpenVINO IR .xml CPU/GPU/NPU ~2-3x AMD GPU ROCm ONNX Runtime GPU ~1.5-2x CPU (any) CPU ONNX Runtime CPU ~1.5x Apple Silicon Note : Detection defaults to cpu_and_ne (CPU + Neural Engine), keeping the GPU free for LLM/VLM inference. Set compute_units: all to include GPU if not running local LLM. How It Works deploy.sh detects your hardware via env_config.HardwareEnv.detect() Installs the matching requirements_{backend}.txt (e.g. CUDA → includes tensorrt ) Pre-converts the default model to the optimal format At runtime, detect.py loads the cached optimized model automatically Falls back to PyTorch if optimization fails Set use_optimized: false to disable auto-conversion and use raw PyTorch. Auto Start Set auto_start: true in the skill config to start detection automatically when Aegis launches. The skill will begin processing frames from the selected camera immediately. auto_start: true model_size: nano fps: 5 Performance Monitoring The skill emits perf_stats events every 50 frames with aggregate timing: {"event": "perf_stats", "total_frames": 50, "timings_ms": { "inference": {"avg": 3.4, "p50": 3.2, "p95": 5.1}, "postprocess": {"avg": 0.15, "p50": 0.12, "p95": 0.31}, "total": {"avg": 3.6, "p50": 3.4, "p95": 5.5} }} Protocol Communicates via JSON lines over stdin/stdout. Aegis → Skill (stdin) {"event": "frame", "frame_id": 42, "camera_id": "front_door", "timestamp": "...", "frame_path": "/tmp/aegis_detection/frame_front_door.jpg", "width": 1920, "height": 1080} Skill → Aegis (stdout) {"event": "ready", "model": "yolo2026n", "device": "mps", "backend": "mps", "format": "coreml", "gpu": "Apple M3", "classes": 80, "fps": 5} {"event": "detections", "frame_id": 42, "camera_id": "front_door", "timestamp": "...", "objects": [ {"class": "person", "confidence": 0.92, "bbox": [100, 50, 300, 400]} ]} {"event": "perf_stats", "total_frames": 50, "timings_ms": {"inference": {"avg": 3.4}}} {"event": "error", "message": "...", "retriable": true} Bounding Box Format [x_min, y_min, x_max, y_max] — pixel coordinates (xyxy). Stop Command {"command": "stop"} Installation The deploy.sh bootstrapper handles everything — Python environment, GPU backend detection, dependency installation, and model optimization. No manual setup required. ./deploy.sh Requirements Files File Backend Key Deps requirements_cuda.txt NVIDIA torch (cu124), tensorrt requirements_mps.txt Apple torch , coremltools requirements_intel.txt Intel torch , openvino requirements_rocm.txt AMD torch (rocm6.2), onnxruntime-rocm requirements_cpu.txt CPU torch (cpu), onnxruntime