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Yolov9c Cs2
Counter-Strike 2 (CS2) player object detection model based on YOLOv9 architecture, capable of recognizing player characters in the game
Downloads 16
Release Time : 4/27/2024
Model Overview
This model is specifically designed to detect player characters in Counter-Strike 2 game scenes, supporting the identification of four target classes: 'c' (Counter-Terrorist), 'ch' (Counter-Terrorist head), 't' (Terrorist), 'th' (Terrorist head)
Model Features
Game-specific Optimization
Object detection model specifically optimized for CS2 game scenes, accurately recognizing player characters in the game
Lightweight Design
Offers multiple model size options (6MB-50MB) to meet deployment requirements under different hardware conditions
Multi-format Support
Supports multiple formats including PyTorch (.pt), ONNX (.onnx), and TensorRT (.engine) for easy deployment across different platforms
Model Capabilities
Real-time Object Detection
Game Scene Analysis
Player Character Recognition
Head Detection
Use Cases
Game Analysis
CS2 Game Replay Analysis
Automatically identifies player positions and character types in game replays
Can be used for tactical analysis or highlight capture
Game Assistant Tool
Provides real-time detection functionality for game developers or players
Helps understand player distribution and behavior patterns in-game
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