# farzaa/DeepLeague

(Open Source) Computer Vision + Deep Learning + League of Legends

Repository: https://github.com/farzaa/DeepLeague
Canonical: https://ross.abutalabs.com/products/deepleague
Language: Python
License Family: other
Last push: 2019-12-18T03:47:57+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3148, "days_push": 2450, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1210, forks 178 (observed 2026-08-28T04:03:59.986863+00:00)

## What it is
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
- artifact type: dataset
- maturity: abandoned
- function: computer-vision, machine-learning, deep-learning, image-processing
- domain: computer-vision, deep-learning, gaming-tools, machine-learning
- platform: python
- tags: esports, league-of-legends, object-detection, yolo, labeled-dataset, minimap-analysis, macos, linux, gpu

## Member repositories
- farzaa/DeepLeague (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:59.986863+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:18:10.640330+00:00, confidence not recorded.
  - readme: https://github.com/farzaa/DeepLeague (fetched 2026-08-28T04:03:59.986863+00:00, sha aba5437be654)
- Data as of 2026-08-30T08:39:29.467469+00:00.