Improve Your Bot with Machine Learning-Based Image Analysis

Machine learning—especially object detection, classification, and segmentation—can dramatically expand what game bots are capable of. Imagine bots that can accurately identify in-game items, enemies, and terrain, or classify player behavior to make smarter and more effective decisions. The hard part is getting started: there is often a ton of information to sort through, and setup can be complicated.

Meet the CVAT auto-annotation tool — your new go-to tool for training ML models.

CVATAAT is here to make that process easier. Built for people who are new to botting, it helps you prepare and train your game data with minimal hassle. It’s free, runs on your own computer, and is focused specifically on gaming use cases. Just feed it your game images, label them, and CVATAAT does the heavy lifting by quickly training and delivering optimized models that you can then use in EyeAuras with the ML-Search trigger.

This is still an early alpha, so things may go wrong. Please report any issues so we can make the model preparation process as simple as possible together. I genuinely believe this is the future, and with this tool I want to clear some of the obstacles out of the way.

Bugfixes and Improvements

• [SendSequence] Added support for mouse wheel up/down. For now, this is available only in SendSequence.
• [Dependencies Viewer/Graph] Now uses a different method to render auras/dependencies. It should be much more responsive now.