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Yolov8s Csgo Player Detection
An object detection model based on YOLOv8, specifically designed to detect players and their head positions in CSGO.
Downloads 216
Release Time : 1/29/2023
Model Overview
This model is trained using the YOLOv8 architecture, specifically for detecting player characters (Counter-Terrorists and Terrorists) and their head positions in CSGO game scenes, suitable for game analysis and computer vision applications.
Model Features
High-Precision Detection
Achieves 88.56% mAP@0.5 on the validation set, accurately identifying players and head positions in the game.
Multi-Class Recognition
Can distinguish between Counter-Terrorists, Terrorists, and their head positions, supporting detection for a total of 4 classes.
Real-Time Performance
Based on the lightweight YOLOv8s architecture, suitable for applications requiring real-time processing.
Model Capabilities
Game Scene Analysis
Player Position Detection
Head Position Recognition
Real-Time Object Detection
Use Cases
Game Analysis
CSGO Player Behavior Analysis
Analyze tactical behaviors and strategies in the game by detecting player positions and head orientations.
Can generate player position heatmaps and movement trajectory analysis.
E-Sports Training Aid
Provide game replay analysis for professional players to help improve aiming and movement skills.
Can quantitatively evaluate aiming accuracy and reaction time.
Computer Vision Applications
Automatic Game Highlight Recording
Automatically detect highlight moments (e.g., headshot kills) and generate highlight clips.
Reduces manual screening time and improves content production efficiency.
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