Using Ultralytics

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 Ultralytics YOLO models (including YOLO26)
• Running inference (detection, segmentation, pose estimation)
• Exporting models to ONNX, TensorRT, CoreML, etc.
• 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):
   • YOLO26x + Batch 128 ≈ 40GB+ VRAM Needed.
   • CPU Training + Large Model = Extremely Slow (Days).
3. BLOCK/WARN:

   "Training YOLO26x with Batch 128 requires ~40GB VRAM (A100 class). Your environment (Laptop GPU/CPU) will likely 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 Ultralytics auto-batch 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=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640

Pattern 2: Predict (CLI)

yolo detect predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'

Pattern 3: Export Model

yolo export model=yolo26n.pt format=onnx

Pattern 4: Train (Python)

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

Pattern 5: Predict (Python)

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
results = model("path/to/image.jpg")

Pattern 6: Segmentation Training

model = YOLO("yolo26n-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.

Read these files on demand 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.