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Yolov8m Csgo Player Detection

Developed by keremberke
A YOLOv8m-based object detection model specifically designed to detect players and their head positions in CS:GO.
Downloads 324
Release Time : 1/29/2023

Model Overview

This model is trained using the YOLOv8m architecture, accurately identifying Counter-Terrorists, Terrorists, and their head positions in CS:GO, suitable for game analysis and content creation.

Model Features

High-precision Detection
Achieves 89.165% mAP@0.5 accuracy in CS:GO player detection tasks.
Multi-class Recognition
Can distinguish between Counter-Terrorists, Terrorists, and their head positions across 4 categories.
Real-time Performance
Optimized based on the YOLOv8 architecture, suitable for real-time game scene analysis.

Model Capabilities

Game scene analysis
Player position detection
Head position recognition
Real-time object detection

Use Cases

Game Analysis
Game Replay Analysis
Analyze player positions and movement trajectories in game replays
Useful for tactical analysis and training improvement
Content Creation
Automatically tag player positions in game videos
Simplifies game video editing and special effects addition
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