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Anyone currently training custom models for R6 object detection? I saw the paste going around for basic enemy detection, but it's clearly struggling with class discrimination, grabbing random map geometry instead of actual operators.
For those trying to scrape data to build a YOLO or similar computer vision stack for a triggerbot or external ESP: the issue isn't usually the detection logic itself, but the label quality and the environment context. If you are just doing raw capture, you are feeding your model too much noise—operator models in Siege have specific textures and silhouette profiles that are distinct from background clutter. You need to filter your frame buffers based on the operator's bounding box constraints and normalize your dataset before it even touches your trainer.
Common bottlenecks when scraping frames for AI training:
If you want to move beyond basic object detection, stop trying to use global image analysis and look into individual model hooks. Using a kernel-level capture method is safer, but keep in mind that excessive frame requests can trigger heuristic flags if you aren't careful with your polling rate.
Anyone else here building a custom model for Siege, or are you guys sticking to standard memory-based ESP for now?
For those trying to scrape data to build a YOLO or similar computer vision stack for a triggerbot or external ESP: the issue isn't usually the detection logic itself, but the label quality and the environment context. If you are just doing raw capture, you are feeding your model too much noise—operator models in Siege have specific textures and silhouette profiles that are distinct from background clutter. You need to filter your frame buffers based on the operator's bounding box constraints and normalize your dataset before it even touches your trainer.
Common bottlenecks when scraping frames for AI training:
- FPS capping during capture causing massive motion blur on the frames.
- Inconsistent lighting values across different maps (e.g., Bank vs. Oregon).
- Overfitting to specific operator loadouts instead of the core human-like silhouette.
If you want to move beyond basic object detection, stop trying to use global image analysis and look into individual model hooks. Using a kernel-level capture method is safer, but keep in mind that excessive frame requests can trigger heuristic flags if you aren't careful with your polling rate.
Don't just collect thousands of images. 500 frames of clean, varied angles are worth more than 50,000 frames of static ADS spam. Use a script to strip frames where no hitbox coordinate is present in the memory to keep your training set lean.
Anyone else here building a custom model for Siege, or are you guys sticking to standard memory-based ESP for now?
