farzaa/DeepLeague resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1210 stars · 178 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DeepLeague is a computer vision and deep learning project that applies YOLO-based object detection to the League of Legends minimap, bundled with a dataset of over 100,000 labeled images for esports AI research. It is built on a custom fork of YAD2K and includes setup scripts for training and running detection.

Use cases

• detect champions on a League of Legends minimap with a neural network
• get a large labeled esports dataset for object detection research
• train YOLO models on game footage frames
• run computer vision experiments on esports video
• analyze player positions from game minimap screenshots

When to choose

• you want a large pre-labeled esports/game imagery dataset
• you need a working example of YOLO applied to game video
• you're researching computer vision in esports

When to avoid

• you need a polished, well-documented library
• you need active maintenance or support
• you need Windows-native setup without WSL
• you want production-grade esports analytics tooling

Facets

dataset · maturity abandoned

computer-vision machine-learning deep-learning image-processing computer-vision deep-learning gaming-tools machine-learning python esports league-of-legends object-detection yolo labeled-dataset minimap-analysis macos linux gpu

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For agents

markdown · JSON · MCP: product_card(name="farzaa/DeepLeague")