Yolo Detection 2026

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](../../lib/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

1. deploy.sh detects your hardware via env_config.HardwareEnv.detect()
2. Installs the matching requirements_{backend}.txt (e.g. CUDA → includes tensorrt)
3. Pre-converts the default model to the optimal format
4. At runtime, detect.py loads the cached optimized model automatically
5. 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 |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: SharpAI
• Source: SharpAI/DeepCamera
• License: MIT
• Homepage: http://www.sharpai.org

Install and usage instructions live in the source repository linked above.