--- title: "caer vs RobustVideoMatting" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jasmcaus-caer-vs-peterl1n-robustvideomatting" tools: ["jasmcaus-caer", "peterl1n-robustvideomatting"] --- # caer vs RobustVideoMatting *GraphCanon updated Jul 31, 2026* ## Verdict Pick caer if caer is noted for its high-performance vision tasks including image and video processing, with GPU support via CUDA; pick RobustVideoMatting if robustVideoMatting is a deep-learning-based video matting tool using recurrent neural networks for real-time processing on videos with temporal memory. [caer](https://caer.readthedocs.io) reports 812 GitHub stars, 108 forks, and 1 open issues, last pushed Jul 25, 2026. [RobustVideoMatting](https://peterl1n.github.io/RobustVideoMatting/) has 9.5k stars, 1.2k forks, and 122 open issues, last pushed Apr 2, 2024. Figures are from public GitHub metadata via [caer's repository](https://github.com/jasmcaus/caer) and [RobustVideoMatting's repository](https://github.com/PeterL1n/RobustVideoMatting). | | [caer](/tools/jasmcaus-caer.md) | [RobustVideoMatting](/tools/peterl1n-robustvideomatting.md) | | --- | --- | --- | | Tagline | High-performance Vision library in Python for scaling research | Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML | | Stars | 812 | 9,452 | | Forks | 108 | 1,199 | | Open issues | 1 | 122 | | Language | Python | Python | | Adopt for | Caer is noted for its high-performance vision tasks including image and video processing, with GPU support via CUDA. | RobustVideoMatting is a deep-learning-based video matting tool using recurrent neural networks for real-time processing on videos with temporal memory. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 | | Categories | Computer Vision | Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [caer](/tools/jasmcaus-caer.md) | [RobustVideoMatting](/tools/peterl1n-robustvideomatting.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 849d | | Open issues (now) | 1 | 122 | | Full report | [trust report](/tools/jasmcaus-caer/trust.md) | [trust report](/tools/peterl1n-robustvideomatting/trust.md) | ## Decision facts: caer - **Adopt for:** Caer is noted for its high-performance vision tasks including image and video processing, with GPU support via CUDA. ## Decision facts: RobustVideoMatting - **Hosting:** self hosted - **Pricing:** freemium - The tool is freely available under GPL-3.0, with no associated costs. - **Requirements:** A relevant inference framework such as PyTorch or TensorFlow must be installed.; The tool requires a GPU for optimal performance, particularly for handling high-resolution videos. - **Adopt for:** RobustVideoMatting is a deep-learning-based video matting tool using recurrent neural networks for real-time processing on videos with temporal memory. - **License detail:** GPL-3.0 ## Choose when ### Choose caer if… - License: caer is MIT, RobustVideoMatting is GPL-3.0. - Tags unique to caer: artificial-intelligence, augmentation, cuda, data-science. - If you are working on projects that require scaling computer vision research efforts without excessive boilerplate code, Caer offers streamlined solutions. ### Choose RobustVideoMatting if… - License: RobustVideoMatting is GPL-3.0, caer is MIT. - Pricing: The tool is freely available under GPL-3.0, with no associated costs.. - Requirements: A relevant inference framework such as PyTorch or TensorFlow must be installed.; The tool requires a GPU for optimal performance, particularly for handling high-resolution videos.. - Tags unique to RobustVideoMatting: machine-learning, matting. - When working with human video matting that requires high frames per second, as it can achieve 4K 76FPS and HD 104FPS on Nvidia GTX 1080 Ti GPU. ## When NOT to use caer - Avoid using caer if you are restricted to Python versions lower than 3.6, or when adherence to a specific older Python version is critical to your project. - If compatibility with only open-source libraries is needed and CUDA support is not required, other more limited scope tools might be a better choice. ## When NOT to use RobustVideoMatting - If you require matting capabilities that do not focus on human-like targets, as RVM is specifically designed with this in mind. - In scenarios where a model smaller than the MobileNetv3 or ResNet50 options provided by the tool cannot be used due to memory constraints or speed requirements. ## Common questions ### What is the difference between caer and RobustVideoMatting? caer: High-performance Vision library in Python for scaling research. RobustVideoMatting: Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML. See the comparison table for live GitHub stats and shared categories. ### When should I choose caer over RobustVideoMatting? Choose caer over RobustVideoMatting when License: caer is MIT, RobustVideoMatting is GPL-3.0; Tags unique to caer: artificial-intelligence, augmentation, cuda, data-science; If you are working on projects that require scaling computer vision research efforts without excessive boilerplate code, Caer offers streamlined solutions. ### When should I choose RobustVideoMatting over caer? Choose RobustVideoMatting over caer when License: RobustVideoMatting is GPL-3.0, caer is MIT; Pricing: The tool is freely available under GPL-3.0, with no associated costs.; Requirements: A relevant inference framework such as PyTorch or TensorFlow must be installed.; The tool requires a GPU for optimal performance, particularly for handling high-resolution videos.; Tags unique to RobustVideoMatting: machine-learning, matting; When working with human video matting that requires high frames per second, as it can achieve 4K 76FPS and HD 104FPS on Nvidia GTX 1080 Ti GPU. ### When should I avoid caer? Avoid using caer if you are restricted to Python versions lower than 3.6, or when adherence to a specific older Python version is critical to your project. If compatibility with only open-source libraries is needed and CUDA support is not required, other more limited scope tools might be a better choice. ### When should I avoid RobustVideoMatting? If you require matting capabilities that do not focus on human-like targets, as RVM is specifically designed with this in mind. In scenarios where a model smaller than the MobileNetv3 or ResNet50 options provided by the tool cannot be used due to memory constraints or speed requirements. ### Is caer or RobustVideoMatting more popular on GitHub? RobustVideoMatting has more GitHub stars (9,452 vs 812). Stars measure visibility, not whether either tool fits your constraints. ### Are caer and RobustVideoMatting open source? Yes - both are open-source projects on GitHub (caer: MIT, RobustVideoMatting: GPL-3.0). ### Where can I find alternatives to caer or RobustVideoMatting? GraphCanon lists graph-backed alternatives at [caer alternatives](/tools/jasmcaus-caer/alternatives) and [RobustVideoMatting alternatives](/tools/peterl1n-robustvideomatting/alternatives) ([caer markdown twin](/tools/jasmcaus-caer/alternatives.md), [RobustVideoMatting markdown twin](/tools/peterl1n-robustvideomatting/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/jasmcaus-caer-vs-peterl1n-robustvideomatting.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, caer or RobustVideoMatting? caer: Very active. RobustVideoMatting: 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 caer and RobustVideoMatting? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [caer trust report](/tools/jasmcaus-caer/trust); [RobustVideoMatting trust report](/tools/peterl1n-robustvideomatting/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jasmcaus-caer`](/api/graphcanon/graph?tool=jasmcaus-caer) - 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/_