--- title: "mxnet vs caffe" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/apache-mxnet-vs-bvlc-caffe" tools: ["apache-mxnet", "bvlc-caffe"] --- # mxnet vs caffe *GraphCanon updated Aug 3, 2026* ## Verdict Pick mxnet if apache MXNet is a deep learning framework that prioritizes efficiency and flexibility, allowing for the mix of symbolic and imperative programming techniques; pick caffe if caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency. [mxnet](https://mxnet.apache.org) reports 21k GitHub stars, 6.7k forks, and 2.0k open issues, last pushed Oct 25, 2023. [caffe](http://caffe.berkeleyvision.org/) has 35k stars, 18k forks, and 1.5k open issues, last pushed Jul 31, 2024. Figures are from public GitHub metadata via [mxnet's repository](https://github.com/apache/mxnet) and [caffe's repository](https://github.com/BVLC/caffe). | | [mxnet](/tools/apache-mxnet.md) | [caffe](/tools/bvlc-caffe.md) | | --- | --- | --- | | Tagline | Lightweight, Portable, Flexible Distributed/Mobile Deep Learning Framework | Caffe is a fast open framework for deep learning. | | Stars | 20,817 | 34,573 | | Forks | 6,690 | 18,443 | | Open issues | 2,007 | 1,471 | | Language | C++ | C++ | | Adopt for | Apache MXNet is a deep learning framework that prioritizes efficiency and flexibility, allowing for the mix of symbolic and imperative programming techniques. | Caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Caffe is available under the BSD 2-Clause license. | | Categories | Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mxnet](/tools/apache-mxnet.md) | [caffe](/tools/bvlc-caffe.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Dormant (18%) | | Days since push | 1012d | 732d | | Archived on GitHub | Yes | No | | Open issues (now) | 2.0k | 1.5k | | Full report | [trust report](/tools/apache-mxnet/trust.md) | [trust report](/tools/bvlc-caffe/trust.md) | ## Decision facts: mxnet - **Pricing:** freemium - Open-source, open-access framework with advanced services potentially requiring proprietary add-ons or cloud service costs. - **Requirements:** MXNet is known for its lightweight nature and efficient memory management, making it suitable for deployment on various hardware configurations. - **Adopt for:** Apache MXNet is a deep learning framework that prioritizes efficiency and flexibility, allowing for the mix of symbolic and imperative programming techniques. ## Decision facts: caffe - **Pricing:** freemium - Free to use under open source licensing with no monetary charges. - **Adopt for:** Caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency. - **License detail:** Caffe is available under the BSD 2-Clause license. ## Choose when ### Choose mxnet if… - License: mxnet is Apache-2.0, caffe is Other. - Pricing: Open-source, open-access framework with advanced services potentially requiring proprietary add-ons or cloud service costs.. - Requirements: MXNet is known for its lightweight nature and efficient memory management, making it suitable for deployment on various hardware configurations.. - Tags unique to mxnet: auto hybridization, distributed-computing, flexible, lightweight. - You prefer to mix symbolic and imperative programming styles in your deep learning projects for maximum productivity and performance. ### Choose caffe if… - License: caffe is Other, mxnet is Apache-2.0. - Pricing: Free to use under open source licensing with no monetary charges.. - Tags unique to caffe: machine-learning, vision. - Also covers Computer Vision. - - You need a framework that supports high-performance convolutional networks particularly suited for image classification ## When NOT to use mxnet - If you require a framework with more out-of-the-box models and easier-to-use libraries, since MXNet focuses on flexibility and efficiency over convenience in pre-built functionalities. - You are focusing exclusively on one particular programming language (other than Python), as while MXNet supports multiple languages, most community support and updates center around its Python API. ## When NOT to use caffe - - Your primary task involves natural language processing rather than computer vision challenges, where specialized frameworks might outperform Caffe - - You seek a framework that integrates seamlessly with Python for both training and inference, as Caffe relies heavily on C++ for its core operations ## Common questions ### What is the difference between mxnet and caffe? mxnet: Lightweight, Portable, Flexible Distributed/Mobile Deep Learning Framework. caffe: Caffe is a fast open framework for deep learning.. See the comparison table for live GitHub stats and shared categories. ### When should I choose mxnet over caffe? Choose mxnet over caffe when License: mxnet is Apache-2.0, caffe is Other; Pricing: Open-source, open-access framework with advanced services potentially requiring proprietary add-ons or cloud service costs.; Requirements: MXNet is known for its lightweight nature and efficient memory management, making it suitable for deployment on various hardware configurations.; Tags unique to mxnet: auto hybridization, distributed-computing, flexible, lightweight; You prefer to mix symbolic and imperative programming styles in your deep learning projects for maximum productivity and performance. ### When should I choose caffe over mxnet? Choose caffe over mxnet when License: caffe is Other, mxnet is Apache-2.0; Pricing: Free to use under open source licensing with no monetary charges.; Tags unique to caffe: machine-learning, vision; Also covers Computer Vision; - You need a framework that supports high-performance convolutional networks particularly suited for image classification. ### When should I avoid mxnet? If you require a framework with more out-of-the-box models and easier-to-use libraries, since MXNet focuses on flexibility and efficiency over convenience in pre-built functionalities. You are focusing exclusively on one particular programming language (other than Python), as while MXNet supports multiple languages, most community support and updates center around its Python API. ### When should I avoid caffe? - Your primary task involves natural language processing rather than computer vision challenges, where specialized frameworks might outperform Caffe - You seek a framework that integrates seamlessly with Python for both training and inference, as Caffe relies heavily on C++ for its core operations ### Is mxnet or caffe more popular on GitHub? caffe has more GitHub stars (34,573 vs 20,817). Stars measure visibility, not whether either tool fits your constraints. ### Are mxnet and caffe open source? Yes - both are open-source projects on GitHub (mxnet: Apache-2.0, caffe: Other). ### Where can I find alternatives to mxnet or caffe? GraphCanon lists graph-backed alternatives at [mxnet alternatives](/tools/apache-mxnet/alternatives) and [caffe alternatives](/tools/bvlc-caffe/alternatives) ([mxnet markdown twin](/tools/apache-mxnet/alternatives.md), [caffe markdown twin](/tools/bvlc-caffe/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/apache-mxnet-vs-bvlc-caffe.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mxnet or caffe? mxnet: Archived. caffe: 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 mxnet and caffe? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mxnet trust report](/tools/apache-mxnet/trust); [caffe trust report](/tools/bvlc-caffe/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=apache-mxnet`](/api/graphcanon/graph?tool=apache-mxnet) - 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/_