YOLO Getting Started: History, Version Comparison and Environment Setup

Tue, 05 May 2026 00:00:00 +0000

Learning Path and Version Selection Guide

Version Selection Guide

Version	Release Date	Development Team	Use Cases	Recommendation Index
YOLO26	2026.01	Ultralytics Official	Edge deployment, CPU inference, industrial applications	⭐⭐⭐⭐⭐
YOLOv8	2023.01	Ultralytics Official	Beginner learning, complete ecosystem, general scenarios	⭐⭐⭐⭐⭐
YOLO11	2024.09	Ultralytics Official	Efficiency optimization, lightweight deployment	⭐⭐⭐⭐
YOLOv10	2024.05	Tsinghua University	Research exploration, NMS-free end-to-end	⭐⭐⭐⭐
YOLOv9	2024.01	National Taiwan University	High precision, small object detection	⭐⭐⭐⭐
YOLOv12	2025.02	Buffalo University + Chinese Academy of Sciences	Attention mechanism research	⭐⭐⭐

Learning Path Recommendations

Beginner Stage (1-2 weeks): Start with YOLOv8, master basic concepts and API usage
Intermediate Stage (2-3 weeks): Learn custom dataset training, parameter tuning and optimization
Advanced Stage (2-3 weeks): Learn model deployment, engineering implementation
Research Stage (ongoing): Explore new features in YOLO11, YOLO26, YOLOv9/v10/v12

Complete YOLO Development History Timeline

Version	Release Date	Core Innovation	Milestone Significance
YOLOv1	2015.06	Pioneer single-stage detection	Foundation for real-time detection
YOLOv2	2016.12	Batch Normalization, Anchor	Dual improvement in accuracy and speed
YOLOv3	2018.04	Multi-scale detection, residual networks	Industry standard
YOLOv4	2020.04	CSPDarknet, Mosaic	Peak of engineering implementation
YOLOv5	2020.06	PyTorch framework, user-friendly	Highest adoption rate
YOLOv7	2022.07	E-ELAN, reparameterization	Balance between speed and accuracy
YOLOv8	2023.01	C2f, Anchor-Free, unified framework	Ultralytics unified ecosystem
YOLOv9	2024.01	GELAN, PGI programmable gradient	Training efficiency revolution
YOLOv10	2024.05	NMS-free, efficiency-precision tradeoff	End-to-end detection
YOLO11	2024.09	Architecture optimization, parameter reduction	Efficiency optimized version
YOLOv12	2025.02	Area Attention mechanism	Attention architecture
YOLO26	2026.01	DFL-free, NMS-free, 43% CPU optimization	Edge computing new standard

Core Principles and Version Comparison

Ultralytics Official Main Line Versions

YOLOv8 Core Features:

YOLO Quick Start: Model Loading and Inference

Fri, 08 May 2026 00:00:00 +0000

Model Loading and Inference Across Versions

Ultralytics Unified API (Works with v8/11/26)

python


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from ultralytics import YOLO

# ========== YOLOv8 ==========
model_v8 = YOLO("yolov8n.pt") # nano
model_v8 = YOLO("yolov8s.pt") # small
model_v8 = YOLO("yolov8m.pt") # medium
model_v8 = YOLO("yolov8l.pt") # large
model_v8 = YOLO("yolov8x.pt") # extra large

# ========== YOLO11 ==========
model_11 = YOLO("yolo11n.pt") # nano
model_11 = YOLO("yolo11s.pt") # small
model_11 = YOLO("yolo11m.pt") # medium
model_11 = YOLO("yolo11l.pt") # large
model_11 = YOLO("yolo11x.pt") # extra large

# ========== YOLO26 (2026 latest) ==========
model_26 = YOLO("yolo26n.pt") # nano recommended for edge deployment
model_26 = YOLO("yolo26s.pt") # small
model_26 = YOLO("yolo26m.pt") # medium
model_26 = YOLO("yolo26l.pt") # large
model_26 = YOLO("yolo26x.pt") # extra large

Image Detection Hands-on Example

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from ultralytics import YOLO

# Load model (YOLO26 example)
model = YOLO("yolo26n.pt")

# Single image detection
results = model("test.jpg", conf=0.25, iou=0.45)

# Process results
for result in results:
 boxes = result.boxes # Detection boxes
 masks = result.masks # Segmentation masks
 probs = result.probs # Classification probabilities
 
 # Print detection results
 for box in boxes:
 print(f"Class: {result.names[int(box.cls)]}, "
 f"Confidence: {box.conf.item():.3f}, "
 f"Coordinates: {box.xyxy.tolist()[0]}")
 
 # Save visualization results
 result.save("result.jpg")

Video Detection Hands-on Example

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from ultralytics import YOLO

model = YOLO("yolo26n.pt")

# Video file detection
results = model.predict(
 source="input.mp4",
 save=True, # Save result video
 conf=0.3,
 show=False, # Whether to display in real-time
 stream=True # Stream processing to save memory
)

# Process frame by frame
for result in results:
 # Custom post-processing logic
 pass

Real-time Camera Detection

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from ultralytics import YOLO
import cv2

model = YOLO("yolo26n.pt")

# Open camera
cap = cv2.VideoCapture(0) # 0 is default camera

while cap.isOpened():
 ret, frame = cap.read()
 if not ret:
 break
 
 # Inference
 results = model(frame, verbose=False)
 
 # Draw results
 annotated_frame = results[0].plot()
 
 # Display
 cv2.imshow("YOLO Real-time", annotated_frame)
 if cv2.waitKey(1) & 0xFF == ord('q'):
 break

cap.release()
cv2.destroyAllWindows()

Version-specific Code Differences

Feature	YOLOv8	YOLO11	YOLO26	YOLOv9	YOLOv10
Unified API	✅	✅	✅	❌ Separate repo	❌ Separate repo
No NMS	❌	❌	✅	❌	✅
DFL Module	✅	✅	❌ Removed	✅	✅
MuSGD Optimizer	❌	❌	✅	❌	❌
Export Compatibility	Good	Good	Best	Fair	Fair

Results Object API Deep Dive

The model() or model.predict() call returns a list of Results objects. Each Results object encapsulates all inference outputs for a single image. Understanding its internal structure is essential for downstream processing.

Computer Vision on Mi&Bee Blog