Ultralytics (YOLO) Skill

Expert system for Ultralytics YOLO models (Object Detection, Segmentation, Classification).

When to Use This Skill

This skill should be triggered when:

• •Training YOLO models (v8, v9, v10, v11)
• •Running inference (detection, segmentation, pose estimation)
• •Exporting models to ONNX, TensorRT, CoreML, etc.
• •Configuring datasets (COCO, custom YAML)
• •Configuring datasets (COCO, custom YAML)
• •Debugging training pipelines or performance issues
• •Training Config Requests: "Set batch size to 128", "Train on CPU", "Use optimizer SGD"

Logic Flow & Decision Tree

0. Configuration Gatekeeper (The OOM Stopper)

When: User proposes specific training parameters (Batch Size, Device, Image Size). Action:

1. •PAUSE. Perform a "Mental Simulation" of resource usage.
2. •Calculate (Heuristic):
   • •YOLOv8x + Batch 128 ≈ 40GB+ VRAM Needed.
   • •CPU Training + Large Model = Extremely Slow (Days).
3. •BLOCK/WARN:

   "Training YOLOv8x with Batch 128 requires ~40GB VRAM (A100 class). Your environment (Laptop GPU/CPU) will crash (OOM). Recommendation: Batch 4 or 8."

Pre-Flight Checks (Mandatory)

Before starting any training run, you MUST:

1. •

   Check GPU Resources (CUDA/ROCm):

   • •Verify device availability: torch.cuda.is_available()
   • •Check VRAM capacity: nvidia-smi or torch.cuda.get_device_properties(0).total_memory
   • •Auto-Batch Suggestion:
     • •< 6GB VRAM: batch=4 or batch=8
     • •6GB - 12GB VRAM: batch=16
     • •

       12GB VRAM: batch=32+

     • •Tip: Use batch=-1 for YOLOv8's auto-batch feature if unsure.
2. •

   Verify Data Leakage:

   • •Ensure NO overlap between Train and Val image files.

Quick Reference

Common Patterns

Pattern 1: Train Model (CLI)

yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640

Pattern 2: Predict (CLI)

yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'

Pattern 3: Export Model

yolo export model=yolov8n.pt format=onnx

Pattern 4: Train (Python)

from ultralytics import YOLO

model = YOLO("yolov8n.pt")
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

Pattern 5: Predict (Python)

from ultralytics import YOLO

model = YOLO("yolov8n.pt")
results = model("path/to/image.jpg")

Pattern 6: Segmentation Training

model = YOLO("yolov8n-seg.pt")
model.train(data="coco8-seg.yaml", epochs=100)

Reference Files

This skill includes comprehensive documentation in references/:

• •guides.md - Step-by-step guides for common tasks.
• •models.md - Details on available model architectures.
• •datasets.md - How to format and use datasets.
• •tasks.md - Explanation of tasks (Detect, Segment, Classify, Pose, OBB).
• •modes.md - Modes of operation (Train, Val, Predict, Export, Track).
• •detect.md - Specifics for object detection.

Use view_file to read these files for in-depth information.

usage

For Beginners

Start with references/tasks.md to understand the capabilities (Detect, Segment, etc.) and references/modes.md for the workflow (Train -> Val -> Predict).

For Custom Training

Refer to references/datasets.md to ensure your data YAML and directory structure are correct before starting training.

For Deployment

Check references/guides.md (or export documentation) for details on optimizing models for different hardware using yolo export.