model-training

name	model-training
description	Agent-driven YOLO fine-tuning — annotate, train, export, deploy
version	1.0.0
parameters	[{"name":"base_model","label":"Base Model","type":"select","options":["yolo26n","yolo26s","yolo26m","yolo26l"],"default":"yolo26n","description":"Pre-trained model to fine-tune","group":"Training"},{"name":"dataset_dir","label":"Dataset Directory","type":"string","default":"~/datasets","description":"Path to COCO-format dataset (from dataset-annotation skill)","group":"Training"},{"name":"epochs","label":"Training Epochs","type":"number","default":50,"group":"Training"},{"name":"batch_size","label":"Batch Size","type":"number","default":16,"description":"Adjust based on GPU VRAM","group":"Training"},{"name":"auto_export","label":"Auto-Export to Optimal Format","type":"boolean","default":true,"description":"Automatically convert to TensorRT/CoreML/OpenVINO after training","group":"Deployment"},{"name":"deploy_as_skill","label":"Deploy as Detection Skill","type":"boolean","default":false,"description":"Replace the active YOLO detection model with the fine-tuned version","group":"Deployment"}]
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-annotation skill
• 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