YOLOv11 - League of Legends Minimap Detection
🦁 Model Zoo & Performance
This repository contains a collection of YOLOv11 models fine-tuned on high-quality replay data. We provide multiple model sizes to suit different hardware constraints, from real-time edge devices to high-performance GPUs.
| Model | mAP@50 | mAP@50-95 | Precision | Recall |
|---|---|---|---|---|
| yolo11n | 0.773 | 0.640 | 0.931 | 0.704 |
| yolo11s | 0.814 | 0.680 | 0.943 | 0.756 |
| yolo11m | 0.851 | 0.728 | 0.915 | 0.812 |
| yolo11l | 0.852 | 0.738 | 0.947 | 0.802 |
| yolo11x | 0.859 | 0.737 | 0.939 | 0.817 |
Model Description
These models are fine-tuned versions of Ultralytics YOLOv11, trained specifically to detect League of Legends champions on the minimap.
- Task: Object Detection (Minimap Icons)
- Training Data: Trained on boboyes/leagueoflegends-minimap
- Architecture: YOLOv11 (n/s/m/l/x variants)
Intended Use
- Esports Analysis: Automated tracking of champion movements for post-game analytics.
- Content Creation: Auto-cam tools for observing specific lanes or fights.
- Research: Multi-object tracking (MOT) in crowded, low-resolution environments.
⚠️ Anti-Cheat Warning: This model is intended for research and post-game analysis ONLY. Using this for real-time gameplay advantages (scripting/overlay hacks) is strictly against Riot Games' Terms of Service and will result in a permanent account ban.
💻 How to Use
Installation
pip install ultralytics huggingface_hub
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