--- title: "auto-sklearn vs AI-Basketball-Referee" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-sklearn-vs-ayushpai-ai-basketball-referee" tools: ["automl-auto-sklearn", "ayushpai-ai-basketball-referee"] --- # auto-sklearn vs AI-Basketball-Referee *GraphCanon updated Jul 12, 2026* ## Verdict Pick auto-sklearn when tags unique to auto-sklearn: automated-machine-learning, automl, bayesian-optimization, hyperparameter-optimization; pick AI-Basketball-Referee when tags unique to AI-Basketball-Referee: ai, basketball, computer-vision, object-detection. [auto-sklearn](https://automl.github.io/auto-sklearn) reports 8.1k GitHub stars, 1.3k forks, and 210 open issues, last pushed Jun 29, 2026. [AI-Basketball-Referee](https://youtu.be/VZgXUBi_wkM) has 359 stars, 68 forks, and 1 open issues, last pushed Apr 14, 2024. Figures are from public GitHub metadata via [auto-sklearn's repository](https://github.com/automl/auto-sklearn) and [AI-Basketball-Referee's repository](https://github.com/ayushpai/AI-Basketball-Referee). | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [AI-Basketball-Referee](/tools/ayushpai-ai-basketball-referee.md) | | --- | --- | --- | | Tagline | Automated Machine Learning with scikit-learn | AI Basketball Referee | | Stars | 8,119 | 359 | | Forks | 1,326 | 68 | | Open issues | 210 | 1 | | Language | Python | Python | | Adopt for | - | AI-Basketball-Referee is a computer vision system that uses YOLO for basketball detection and pose estimation to improve referee accuracy in real-time by detecting travels and double dribbles with precision. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | - | | Categories | Computer Vision, Developer Tools, Model Training | Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [AI-Basketball-Referee](/tools/ayushpai-ai-basketball-referee.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 817d | | Open issues (now) | 210 | 1 | | Owner type | Organization | User | | Security scan | 22 low (22 low) | No lockfile | | Full report | [trust report](/tools/automl-auto-sklearn/trust.md) | [trust report](/tools/ayushpai-ai-basketball-referee/trust.md) | ## Decision facts: AI-Basketball-Referee - **Adopt for:** AI-Basketball-Referee is a computer vision system that uses YOLO for basketball detection and pose estimation to improve referee accuracy in real-time by detecting travels and double dribbles with precision. ## Choose when ### Choose auto-sklearn if… - Tags unique to auto-sklearn: automated-machine-learning, automl, bayesian-optimization, hyperparameter-optimization. - Also covers Developer Tools, Model Training. - auto-sklearn ships Docker support for self-hosted deployment. ### Choose AI-Basketball-Referee if… - Tags unique to AI-Basketball-Referee: ai, basketball, computer-vision, object-detection. - When needing precise and automated travel and double dribble detections during live games to enhance fairness. - Leaner open-issue backlog (1). ## When NOT to use auto-sklearn - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use AI-Basketball-Referee - If the system needs to run without real-time feedback capabilities, as AI-Basketball-Referee heavily relies on providing immediate detection of violations during gameplay. - In scenarios prioritizing low-cost solutions, given its dependency on a custom YOLO model and extensive labeled data set for accurate detections. ## Common questions ### What is the difference between auto-sklearn and AI-Basketball-Referee? auto-sklearn: Automated Machine Learning with scikit-learn. AI-Basketball-Referee: AI Basketball Referee. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-sklearn over AI-Basketball-Referee? Choose auto-sklearn over AI-Basketball-Referee when Tags unique to auto-sklearn: automated-machine-learning, automl, bayesian-optimization, hyperparameter-optimization; Also covers Developer Tools, Model Training; auto-sklearn ships Docker support for self-hosted deployment. ### When should I choose AI-Basketball-Referee over auto-sklearn? Choose AI-Basketball-Referee over auto-sklearn when Tags unique to AI-Basketball-Referee: ai, basketball, computer-vision, object-detection; When needing precise and automated travel and double dribble detections during live games to enhance fairness; Leaner open-issue backlog (1). ### When should I avoid auto-sklearn? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid AI-Basketball-Referee? If the system needs to run without real-time feedback capabilities, as AI-Basketball-Referee heavily relies on providing immediate detection of violations during gameplay. In scenarios prioritizing low-cost solutions, given its dependency on a custom YOLO model and extensive labeled data set for accurate detections. ### Is auto-sklearn or AI-Basketball-Referee more popular on GitHub? auto-sklearn has more GitHub stars (8,119 vs 359). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-sklearn and AI-Basketball-Referee open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to auto-sklearn or AI-Basketball-Referee? GraphCanon lists graph-backed alternatives at [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) and [AI-Basketball-Referee alternatives](/tools/ayushpai-ai-basketball-referee/alternatives) ([auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md), [AI-Basketball-Referee markdown twin](/tools/ayushpai-ai-basketball-referee/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/automl-auto-sklearn-vs-ayushpai-ai-basketball-referee.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, auto-sklearn or AI-Basketball-Referee? auto-sklearn: Active. AI-Basketball-Referee: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for auto-sklearn and AI-Basketball-Referee? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-sklearn trust report](/tools/automl-auto-sklearn/trust); [AI-Basketball-Referee trust report](/tools/ayushpai-ai-basketball-referee/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-sklearn`](/api/graphcanon/graph?tool=automl-auto-sklearn) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_