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Anyone currently digging into the YOLO training rabbit hole for Rust? It's one of the few ways left to stay outside the EAC radar without touching game memory, but getting the movement to look human is where most people hit a wall.
If your detection app is "snappy" or jittery, you're likely looking at a conflict between your inference speed and your mouse movement algorithm. It's rarely just the dataset unless your confidence scores are bouncing like crazy.
Common bottlenecks when training for Rust:
I've seen similar issues when people try to use generic datasets. Rust players in hazmats or heavy plate armor have very specific silhouettes that confuse standard "person" models. If the app is snappy, try implementing a simple linear interpolation (Lerp) for the movement and check your CUDA utilization.
Who here has successfully implemented a PID controller for their Rust AI project?
If your detection app is "snappy" or jittery, you're likely looking at a conflict between your inference speed and your mouse movement algorithm. It's rarely just the dataset unless your confidence scores are bouncing like crazy.
Common bottlenecks when training for Rust:
- Inference Latency: If you're running a heavy model (like YOLOv8 Large) on a mid-range GPU, the delay between the frame being captured and the box being drawn causes that "laggy" snap.
- Mouse Event Smoothing: Are you using raw mouse_event calls? Without a proper PID controller or Bezier smoothing, the aimbot will snap instantly to the head/neck bone, which looks robotic and triggers every heuristic check in the book.
- Confidence Thresholds: In Rust, lighting changes drastically. If your model isn't trained on diverse datasets (night, fog, desert sun), the detection will flicker on and off, causing the "snappiness" as it loses and re-acquires the target.
Capture Method: Are you using DXCam, MSS, or a hardware fuser? Software capture often adds 15-30ms of lag before the AI even sees the player.
Model Choice: Stick to YOLOv8n or v11n (Nano). You need speed over precision for a game as fast-paced as Rust.
Targeting: Don't just aim for the center of the box. You need to offset for the head based on the height of the bounding box to avoid hitting the stomach every time.
Model Choice: Stick to YOLOv8n or v11n (Nano). You need speed over precision for a game as fast-paced as Rust.
Targeting: Don't just aim for the center of the box. You need to offset for the head based on the height of the bounding box to avoid hitting the stomach every time.
I've seen similar issues when people try to use generic datasets. Rust players in hazmats or heavy plate armor have very specific silhouettes that confuse standard "person" models. If the app is snappy, try implementing a simple linear interpolation (Lerp) for the movement and check your CUDA utilization.
Who here has successfully implemented a PID controller for their Rust AI project?
