# Yolo Detection 2026

YOLO 2026, state-of-the-art real-time object detection

- Repository: https://github.com/SharpAI/DeepCamera (3124 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-17
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026 -g -a claude-code -y`
Codex: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026 -g -a codex -y`
Cursor: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026 -g -a cursor -y`
Gemini CLI: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026 -g -a gemini-cli -y`

## Description

YOLO 2026, state-of-the-art real-time object detection

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/SharpAI/DeepCamera/blob/HEAD/skills/detection/yolo-detection-2026/SKILL.md