Agent Skills
› SharpAI/DeepCamera
› model-training
model-training
GitHub基于Aegis Agent的YOLO模型微调技能，支持从COCO数据集输入、硬件自适应训练到多格式自动导出及部署的全流程闭环。
Trigger Scenarios
需要微调YOLO目标检测模型
将标注好的数据集转换为可部署的检测模型
优化模型以适配特定硬件（如MPS/TensorRT）
Install
npx skills add SharpAI/DeepCamera --skill model-training -g -y
SKILL.md
Frontmatter
{
"name": "model-training",
"version": "1.0.0",
"parameters": [
{
"name": "base_model",
"type": "select",
"group": "Training",
"label": "Base Model",
"default": "yolo26n",
"options": [
"yolo26n",
"yolo26s",
"yolo26m",
"yolo26l"
],
"description": "Pre-trained model to fine-tune"
},
{
"name": "dataset_dir",
"type": "string",
"group": "Training",
"label": "Dataset Directory",
"default": "~\/datasets",
"description": "Path to COCO-format dataset (from dataset-annotation skill)"
},
{
"name": "epochs",
"type": "number",
"group": "Training",
"label": "Training Epochs",
"default": 50
},
{
"name": "batch_size",
"type": "number",
"group": "Training",
"label": "Batch Size",
"default": 16,
"description": "Adjust based on GPU VRAM"
},
{
"name": "auto_export",
"type": "boolean",
"group": "Deployment",
"label": "Auto-Export to Optimal Format",
"default": true,
"description": "Automatically convert to TensorRT\/CoreML\/OpenVINO after training"
},
{
"name": "deploy_as_skill",
"type": "boolean",
"group": "Deployment",
"label": "Deploy as Detection Skill",
"default": false,
"description": "Replace the active YOLO detection model with the fine-tuned version"
}
],
"description": "Agent-driven YOLO fine-tuning — annotate, train, export, deploy",
"capabilities": {
"training": {
"script": "scripts\/train.py",
"description": "Fine-tune YOLO models on custom annotated datasets"
}
}
}
Model Training
Agent-driven custom model training powered by Aegis's Training Agent. Closes the annotation-to-deployment loop: take a COCO dataset from dataset-annotation, fine-tune a YOLO model, auto-export to the optimal format for your hardware, and optionally deploy it as your active detection skill.
What You Get
- Fine-tune YOLO26 — start from nano/small/medium/large pre-trained weights
- COCO dataset input — uses standard format from
dataset-annotationskill - Hardware-aware training — auto-detects CUDA, MPS, ROCm, or CPU
- Auto-export — converts trained model to TensorRT / CoreML / OpenVINO / ONNX via
env_config.py - One-click deploy — replace the active detection model with your fine-tuned version
- Training telemetry — real-time loss, mAP, and epoch progress streamed to Aegis UI
Training Loop (Aegis Training Agent)
dataset-annotation model-training yolo-detection-2026
┌─────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Annotate │───────▶│ Fine-tune YOLO │───────▶│ Deploy custom │
│ Review │ COCO │ Auto-export │ .pt │ model as active │
│ Export │ JSON │ Validate mAP │ .engine│ detection skill │
└─────────────┘ └──────────────────┘ └──────────────────┘
▲ │
└────────────────────────────────────────────────────┘
Feedback loop: better detection → better annotation
Protocol
Aegis → Skill (stdin)
{"event": "train", "dataset_path": "~/datasets/front_door_people/", "base_model": "yolo26n", "epochs": 50, "batch_size": 16}
{"event": "export", "model_path": "runs/train/best.pt", "formats": ["coreml", "tensorrt"]}
{"event": "validate", "model_path": "runs/train/best.pt", "dataset_path": "~/datasets/front_door_people/"}
Skill → Aegis (stdout)
{"event": "ready", "gpu": "mps", "base_models": ["yolo26n", "yolo26s", "yolo26m", "yolo26l"]}
{"event": "progress", "epoch": 12, "total_epochs": 50, "loss": 0.043, "mAP50": 0.87, "mAP50_95": 0.72}
{"event": "training_complete", "model_path": "runs/train/best.pt", "metrics": {"mAP50": 0.91, "mAP50_95": 0.78, "params": "2.6M"}}
{"event": "export_complete", "format": "coreml", "path": "runs/train/best.mlpackage", "speedup": "2.1x vs PyTorch"}
{"event": "validation", "mAP50": 0.91, "per_class": [{"class": "person", "ap": 0.95}, {"class": "car", "ap": 0.88}]}
Setup
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
Version History
- 2264fcb Current 2026-08-20 16:02
