yolo-detection-2026
GitHub基于YOLO 2026模型的实时目标检测技能，支持80+类别识别。具备硬件自动检测与模型格式优化功能，适配NVIDIA GPU、Apple Silicon等多平台以提升推理性能，通过JSON Lines协议进行帧输入与检测结果输出。
Trigger Scenarios
Install
npx skills add SharpAI/DeepCamera --skill yolo-detection-2026 -g -y
SKILL.md
Frontmatter
{
"icon": "assets\/icon.png",
"name": "yolo-detection-2026",
"entry": "scripts\/detect.py",
"deploy": "deploy.sh",
"version": "2.0.0",
"parameters": [
{
"name": "auto_start",
"type": "boolean",
"group": "Lifecycle",
"label": "Auto Start",
"default": false,
"description": "Start this skill automatically when Aegis launches"
},
{
"name": "model_size",
"type": "select",
"group": "Model",
"label": "Model Size",
"default": "nano",
"options": [
"nano",
"small",
"medium",
"large"
],
"description": "Larger models are more accurate but slower"
},
{
"max": 1,
"min": 0.1,
"name": "confidence",
"type": "number",
"group": "Model",
"label": "Confidence Threshold",
"default": 0.8
},
{
"name": "classes",
"type": "string",
"group": "Model",
"label": "Detect Classes",
"default": "person,car,dog,cat",
"description": "Comma-separated COCO class names (80 classes available)"
},
{
"name": "fps",
"type": "select",
"group": "Performance",
"label": "Processing FPS",
"default": 5,
"options": [
0.2,
0.5,
1,
3,
5,
15
],
"description": "Frames per second — higher = more CPU\/GPU usage"
},
{
"name": "device",
"type": "select",
"group": "Performance",
"label": "Inference Device",
"default": "auto",
"options": [
"auto",
"cpu",
"cuda",
"mps",
"rocm"
],
"description": "auto = best available GPU, else CPU"
},
{
"name": "use_optimized",
"type": "boolean",
"group": "Performance",
"label": "Hardware Acceleration",
"default": true,
"description": "Auto-convert model to optimized format for faster inference"
},
{
"name": "compute_units",
"type": "select",
"group": "Performance",
"label": "Apple Compute Units",
"default": "auto",
"options": [
"auto",
"cpu_and_ne",
"all",
"cpu_only",
"cpu_and_gpu"
],
"platform": "macos",
"description": "CoreML compute target — 'auto' routes to Neural Engine (NPU), leaving GPU free for LLM\/VLM"
}
],
"description": "YOLO 2026 — state-of-the-art real-time object detection",
"capabilities": {
"live_detection": {
"script": "scripts\/detect.py",
"description": "Real-time object detection on live camera frames"
}
},
"requirements": {
"torch": ">=2.4.0",
"python": ">=3.9",
"platforms": [
"linux",
"macos",
"windows"
],
"ultralytics": ">=8.3.0"
}
}
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. Setcompute_units: allto include GPU if not running local LLM.
How It Works
deploy.shdetects your hardware viaenv_config.HardwareEnv.detect()- Installs the matching
requirements_{backend}.txt(e.g. CUDA → includestensorrt) - Pre-converts the default model to the optimal format
- At runtime,
detect.pyloads 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 |
Version History
- 2264fcb Current 2026-08-20 16:02
