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Yolo11n Cs2
A lightweight Counter-Strike 2 player detection model based on YOLOv11, suitable for real-time object detection scenarios
Downloads 22
Release Time : 1/28/2025
Model Overview
This model is specifically designed for player detection in Counter-Strike 2 (CS2), capable of identifying player characters in game scenes with support for 4 label categories (c, ch, t, th). Implemented using the Ultralytics framework and PyTorch, it offers multiple size options from Nano to XLarge.
Model Features
Lightweight Design
The smallest yolo11n_cs2 model is only 6MB, ideal for deployment in resource-constrained environments
Multiple Size Options
Offers five model sizes from Nano (6MB) to XLarge (109MB) to meet various accuracy and performance needs
High-quality Annotated Data
Trained on a meticulously annotated dataset from 127+ game scenes, ensuring high labeling quality
Multi-format Support
Supports deployment in PyTorch (.pt), ONNX (.onnx), and TensorRT (.engine) formats
Model Capabilities
Game scene object detection
Real-time player recognition
Multi-category classification (c, ch, t, th)
Use Cases
Game Analysis
Real-time Player Detection
Detects and marks player positions in real-time during CS2 gameplay
Accurately identifies player characters and their respective teams
Game Replay Analysis
Batch processes game replays to analyze player behavior
E-sports
Commentary Assistance
Provides real-time player position information for e-sports commentators
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