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Yolov8s Cs2
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Yolov8s Cs2

Developed by Vombit
An object detection model based on YOLOv8 and YOLOv9, specifically designed for player detection in Counter-Strike 2.
Object Detection
PyTorch
#Game Object Detection#Low Latency Inference#FPS Game Specialized
Downloads 17
Release Time : 4/27/2024

Model Overview

This model is used to detect players in Counter-Strike 2, supporting four labels: 'c' (Counter-Terrorist player), 'ch' (Counter-Terrorist player head), 't' (Terrorist player), 'th' (Terrorist player head).

Model Features

Lightweight Model
Offers multiple versions ranging from 6MB to 50MB, suitable for different hardware configurations.
High-Precision Detection
Trained on data from over 70 matches, capable of accurately identifying players and their heads in the game.
Multi-Format Support
Supports various model formats such as PT, ONNX, and ENGINE for easy deployment in different environments.

Model Capabilities

Game Player Detection
Player Head Detection
Real-Time Object Detection

Use Cases

Game Analysis
Game Replay Analysis
Analyze Counter-Strike 2 game replays to automatically identify player positions and statuses.
Useful for tactical analysis or highlight capture.
Real-Time Game Monitoring
Detect player positions in real-time during gameplay.
Useful for live streaming or game assistance tools.
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