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BattleBit Remastered player object detection using yolov8

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StrateimTech/BattlebitDL

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BattleBitDL (BattleBit Deep learning)

Attempts to mark/identify objects(players) both enemies and teammates using YoloV8's object detection on a custom dataset.

How to use

  • Please consult Requirements first.
  • Read all TODO comments in Program.cs (Replace ModelPath to a absolute path!)
  • Attempt to run (Running in a debug state will decrease performance exponentially!)
  • If running it'll attempt to take a full screenshot of your desktop (Monitor 0) every couple of milliseconds & process it through yolov8.

Model

The model provided in onnx format is a YoloV8 small model trained on 1.25k images at 1920x1088 for about 425~ epochs (Original's are 1920x1080). Model training results

Model youtube showcase

Model Youtube
Model dataset will NOT be released!

Requirements

  • Windows Only
  • Nvidia GPU
  • Powerful CPU (Anything within the last couple of years)
  • CUDA 11.X, cuDNN 11.X, zlib 1.2.3 installed / linked to path
  • Knowledge

NuGet Dependencies

  • YoloV8.NET
  • Emgu.CV
    • Emgu.CV.Bitmap
    • Emgu.CV.Runtime.Windows
  • Microsoft.ML.OnnxRuntime.GPU
  • System.Drawing.common

NOTE

This proof of concept is not viable for cheating since it has very bad predictions (camouflaged players, and high prediction delay 50-80ms). please do not cheat on battlebit :)

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