yolo-pipeline

name	yolo-pipeline
description	YOLO detection, segmentation, classification, and pose estimation setup, training workflow, evaluation metrics, and XAI verification
version	1.0.0
tags	["yolo","ultralytics","detection","segmentation","classification","deep-learning","mAP","IoU"]

YOLO Pipeline

When to Use This Skill

• Detecting objects with bounding boxes in images or video
• Segmenting objects at pixel level
• Classifying entire images into categories
• Estimating human body pose (keypoints)
• Training custom YOLO models on domain-specific data
• Evaluating model performance with IoU/mAP metrics

Decision Framework

Which YOLO Task?

What output do you need?
├── "What objects are here and WHERE?" (bounding boxes)
│   └── ✅ Detection — model('img.jpg') with yolov8n.pt
│
├── "Exact pixel-level shape of each object"
│   └── ✅ Segmentation — model('img.jpg') with yolov8n-seg.pt
│
├── "What IS this image overall?" (single label)
│   └── ✅ Classification — model('img.jpg') with yolov8n-cls.pt
│
├── "What pose is this person in?" (17 keypoints)
│   └── ✅ Pose Estimation — model('img.jpg') with yolov8n-pose.pt
│
└── "Rotated/angled objects" (ships, aircraft)
    └── ✅ Oriented Bounding Boxes (OBB) — yolov8n-obb.pt

Semantic vs Instance Segmentation

Type	Question	Output	Can Count Individuals?
Semantic	"What’s in the scene?"	One color per class	❌ No (5 sheep = one green blob)
Instance (YOLO)	"Which objects where?"	Unique ID per object	✅ Yes (5 sheep = 5 different colors)

YOLO does Instance Segmentation — each object gets its own mask and identity.

Pre-trained Dataset Reference

Dataset	Source	Classes	Typical Use
COCO	Microsoft	80	General object detection
ImageNet	Stanford	1000	Image classification
DOTAv1	Wuhan Univ.	15	Aerial/satellite OBB

When Classic CV Fails → Use Deep Learning

Condition	Classic CV	YOLO/DL
High contrast, uniform light	✅ Works	Overkill
Shadows, uneven lighting	❌ Fails	✅ Robust
Touching/overlapping objects	❌ Fails	✅ Handles
Diverse viewpoints	❌ Unreliable	✅ Generalizes
Need to detect 80+ classes	❌ Impractical	✅ Built-in

YOLO Detection — The 3-Step Pipeline

Step 1: Grid Division (Localization)

• Image is divided into grid cells (e.g., 7×7)
• Each cell is responsible for detecting objects whose center falls within it
• "You Only Look Once" — each cell handles its own region

Step 2: Anchor Box Prediction

• Each cell generates Anchor Boxes of different aspect ratios
• For each anchor: confidence score + class probabilities + bbox coordinates
• YOLO predicts: [center_x, center_y, width, height] (normalized 0-1)

Step 3: Non-Maximum Suppression (NMS)

• Multiple overlapping boxes around the same object → keep only the best one
• Low-confidence boxes are filtered first
• Among remaining overlapping boxes, NMS keeps the highest-confidence box

Training Workflow

Data Preparation Checklist

1. Collect images — diverse angles, lighting, backgrounds
2. Label data — Tools: makesense.ai (free, web-based), CVAT, Roboflow
3. YOLO label format (one .txt per image):

   [class_index] [center_x] [center_y] [width] [height]
   Example: 1 0.45 0.60 0.30 0.40
   

   All coordinates normalized to 0-1 range.
4. Classification: no labels needed — folder name IS the label

   dataset/
   ├── train/
   │   ├── COVID/       ← folder name = class label
   │   └── Normal/
   └── val/
       ├── COVID/
       └── Normal/
   

5. Data Augmentation — use Albumentations (auto-adjusts bbox/polygon coords)

Key Training Parameters

Parameter	What It Controls	Guidance
Learning Rate	Step size for weight updates	Too high = overshoots, too low = slow convergence
Epochs	Full passes through dataset	More is not always better (overfitting risk)
Batch Size	Images per training step	Limited by GPU memory. 16-32 typical
imgsz	Input image resolution	640 default. Higher = better accuracy, slower

Transfer Learning

Don't train from scratch. Use pre-trained weights:

from ultralytics import YOLO
model = YOLO('yolov8n.pt')  # Pre-trained on COCO
model.train(data='my_data.yaml', epochs=100, imgsz=640)

Transfer Learning = standing on giants' shoulders. The model already knows edges, textures, shapes from millions of images. You only retrain the final layers for your specific task.

Saved Weights

File	Contains	Use For
best.pt	Weights at best validation accuracy	✅ Production / inference
last.pt	Weights at final epoch	Resume interrupted training only

Always use best.pt for inference. last.pt may have started overfitting.

Evaluation Metrics

Confusion Matrix Components

	Predicted Positive	Predicted Negative
Actually Positive	TP (correct detection)	FN (missed detection — WORST in medical)
Actually Negative	FP (false alarm)	TN (not used in object detection)

TN is not used in object detection systems — "correctly identifying nothing" is not meaningful.

IoU (Intersection over Union)

IoU = Area of Overlap / Area of Union

• Range: 0 (no overlap) to 1 (perfect match)
• IoU > 0.50 = detection counts as True Positive (standard threshold)
• Higher IoU thresholds = stricter evaluation

mAP (Mean Average Precision)

Metric	Threshold	Strictness
mAP@0.50	50% overlap required	Standard
mAP@0.50:0.95	Average across 50%-95%	Very strict, comprehensive

Precision, Recall, F1

Precision = TP / (TP + FP)    — "Of all detections, how many are correct?"
Recall    = TP / (TP + FN)    — "Of all real objects, how many did we find?"
F1        = 2 × (P × R) / (P + R)  — Harmonic mean

Overfitting Check

If training loss keeps dropping but validation loss plateaus or rises → overfitting. Solutions:

• More data augmentation
• Fewer epochs
• Regularization (dropout, weight decay)

XAI (Explainable AI) — EigenCAM

DL models are black boxes — they give answers but don't explain why.

Heatmap Color	Meaning
🔴 Hot (Red/Yellow)	Model's decision focus — high attention
🔵 Cold (Blue/Purple)	Irrelevant to model's decision

Why it matters in medicine:

• Verify the model looks at the lesion, not the hospital label in the corner
• Prove to clinicians that the AI decision is based on actual pathology
• Required for regulatory approval in many jurisdictions

Common Parameters

from ultralytics import YOLO

model = YOLO('yolov8n.pt')
results = model(
    source='image.jpg',    # or '0' for webcam, or video path
    show=True,             # display results
    save=True,             # save to disk
    conf=0.25,             # minimum confidence threshold
)

Industrial Lighting Systems

Light Type	Setup	Best For
Array/Screen	Front-facing flat	Surface defects
Ring Light	Around camera lens	Small parts, uniform light
Back Light	Behind object	Silhouette, hole detection
Bar Light	Angled beam	Directional surface inspection
Dome Light	Surrounds object	Eliminating reflections on metal/shiny surfaces