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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...
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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.
These results come from the real replay data tests including full occlusions, which are impossible to detect without additional metadata(e.g. prev frames, death logs) which significantly degrade the numbers.
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1 sourceVerified Sep 9
Model artifacts
5 artifactsSource excerpts
2 excerpts| 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 |
These models are fine-tuned versions of Ultralytics YOLOv11, trained specifically to detect League of Legends champions on the minimap.
⚠️ 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.
pip install ultralytics huggingface_hub
yolo11n-minimap.pt
pt · 5.34 MB · SHA-256 7c4de7c2131d…9bd3 · Hugging Face
--- license: cc-by-nc-4.0 library_name: ultralytics tags: - object-detection - yolo - yolo11 - league-of-legends - esports - computer-vision datasets: - boboyes/leagueoflegends-minimap metrics: - mAP pipeline_tag: object-detection --- # 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. These results come from the real replay data tests including full occlusions, which are impossible to detect without additional metadata(e.g. prev frames, death logs) which significantly degrade the numbers. | 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](https://huggingface.co/datasets/boboyes/leagueoflegends-minimap) + finetuned on real replay data(check the github repo) * **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 ```bash pip install ultralytics huggingface_hub
Source context: 215 downloads · 4 likes · Pipeline object-detection · Library ultralytics · Repo boboyes/leagueoflegends-minimap-detection