The document describes the YOLOv8 object detection architecture, including its backbone, neck, and head components, which work together for feature extraction, combination, and detection. It details the roles of different blocks, such as convolutional, SPPF, and detect blocks, along with hyper-parameter settings that affect the model's depth and width. Overall, YOLOv8 is presented as a powerful and efficient evolution of its predecessors in object detection.

1. UNDERSTANDING THE YOLOV8
ARCHITECTURE
This slide provides an overview of the YOLOv8 object detection architecture, its key components, and how they work together.

4. INTRODUCTION TO YOLOV8
• Overview of YOLOv8
• YOLOv8 Architecture
• Backbone
• Neck
• Head

5. THE YOLOV8 ARCHITECTURE
The Backbone The Neck The Head

6. BACKBONE:
FEATURE
EXTRACTION

7. NECK: FEATURE COMBINATION
Upsampling Layer Concatenation Channel Reduction
Multi-Scale Features Spatial Pyramid Pooling

8. HEAD: OBJECT DETECTION
Class Prediction Accuracy
Bounding Box Regression Accuracy
Non-Maximum Suppression Threshold
Object Detection Speed (FPS)

9. CONVOLUTIONAL BLOCKS
• 2D Convolutional Layer
• Batch Normalization
• SiLU Activation Function

10. C2F BLOCK
Convolutional Blocks and
Bottleneck Blocks
Shortcut and N Parameters Depth Multiple and Bottleneck
Blocks
Convolutional Block after Bottleneck Blocks Role in YOLOv8 Architecture

11. SPPF BLOCK
SPPF Block Structure Spatial Pyramid Pooling Improved Speed Feature Aggregation

12. DETECT BLOCK
The Detect Block
Bounding Box
Prediction Track
Class Prediction Track
Anchor-free Prediction

13. HYPER-PARAMETERS
Hyper-parameter Description
Depth Multiple
Determines the number of Bottleneck Blocks in
the C2F (Concentrate to Features) Block. A
higher Depth Multiple results in a deeper
network architecture.
Width Multiple
Determines the output channels of each
convolutional layer. A higher Width Multiple
increases the model's capacity to learn more
complex features.
Max Channels
Sets the maximum number of channels that can
be used in the convolutional layers. This helps
control the model's complexity and memory
footprint.

14. “THE YOLOV8 ARCHITECTURE IS
A POWERFUL AND EFFICIENT
OBJECT DETECTION MODEL
THAT BUILDS UPON THE
SUCCESS OF PREVIOUS YOLO
VERSIONS.”