--- title: "DeepLearningExamples vs ncnn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-deeplearningexamples-vs-tencent-ncnn" tools: ["nvidia-deeplearningexamples", "tencent-ncnn"] --- # DeepLearningExamples vs ncnn *GraphCanon updated Aug 17, 2026* ## Verdict Pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings; pick ncnn if ncnn is a high-performance framework for deep learning inference on mobile platforms written in C++, supporting conversion from multiple DL frameworks via pnnx. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) reports 15k GitHub stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. [ncnn](https://github.com/Tencent/ncnn) has 24k stars, 4.5k forks, and 1.2k open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples) and [ncnn's repository](https://github.com/Tencent/ncnn). | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [ncnn](/tools/tencent-ncnn.md) | | --- | --- | --- | | Tagline | State-of-the-Art Deep Learning scripts for various applications | High-performance neural network inference framework optimized for mobile platforms | | Stars | 14,844 | 23,644 | | Forks | 3,408 | 4,475 | | Open issues | 321 | 1,215 | | Language | Jupyter Notebook | C++ | | Adopt for | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | ncnn is a high-performance framework for deep learning inference on mobile platforms written in C++, supporting conversion from multiple DL frameworks via pnnx. | | Persona | - | - | | Runtime | - | - | | License | - | Other, details not specified within the provided repository content. | | Categories | Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [ncnn](/tools/tencent-ncnn.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 734d | 0d | | Open issues (now) | 321 | 1.2k | | Stars delta | +14 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | [trust report](/tools/tencent-ncnn/trust.md) | ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Decision facts: ncnn - **Pricing:** unknown - **Requirements:** Requires pnnx for exporting PyTorch models to ncnn. - **Adopt for:** ncnn is a high-performance framework for deep learning inference on mobile platforms written in C++, supporting conversion from multiple DL frameworks via pnnx. - **License detail:** Other, details not specified within the provided repository content. ## Choose when ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; ncnn is C++. - Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, large language models. - Also covers Model Training. - The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ### Choose ncnn if… - ncnn is primarily C++; DeepLearningExamples is Jupyter Notebook. - Requirements: Requires pnnx for exporting PyTorch models to ncnn.. - Tags unique to ncnn: android, arm-neon, artificial-intelligence, caffe. - For users requiring fast inference speeds optimized for mobile devices such as Android and iOS. ## When NOT to use DeepLearningExamples - Avoid using DeepLearningExamples if you do not have access to NVIDIA GPUs, as it is heavily optimized for these specific hardware configurations to provide maximum utilization of Tensor Cores. - If your project requires frameworks that are less common (e.g., MXNet or PaddlePaddle) without the same level of support as PyTorch and TensorFlow on this platform, consider other repositories that n ## When NOT to use ncnn - If working in an environment where GPU acceleration on desktop or server is more beneficial than CPU efficiency. - For tasks that demand extensive training within the framework itself, as ncnn focuses on inference rather than training capabilities. ## Common questions ### What is the difference between DeepLearningExamples and ncnn? DeepLearningExamples: State-of-the-Art Deep Learning scripts for various applications. ncnn: High-performance neural network inference framework optimized for mobile platforms. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepLearningExamples over ncnn? Choose DeepLearningExamples over ncnn when DeepLearningExamples is primarily Jupyter Notebook; ncnn is C++; Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, large language models; Also covers Model Training; The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ### When should I choose ncnn over DeepLearningExamples? Choose ncnn over DeepLearningExamples when ncnn is primarily C++; DeepLearningExamples is Jupyter Notebook; Requirements: Requires pnnx for exporting PyTorch models to ncnn.; Tags unique to ncnn: android, arm-neon, artificial-intelligence, caffe; For users requiring fast inference speeds optimized for mobile devices such as Android and iOS. ### When should I avoid DeepLearningExamples? Avoid using DeepLearningExamples if you do not have access to NVIDIA GPUs, as it is heavily optimized for these specific hardware configurations to provide maximum utilization of Tensor Cores. If your project requires frameworks that are less common (e.g., MXNet or PaddlePaddle) without the same level of support as PyTorch and TensorFlow on this platform, consider other repositories that n ### When should I avoid ncnn? If working in an environment where GPU acceleration on desktop or server is more beneficial than CPU efficiency. For tasks that demand extensive training within the framework itself, as ncnn focuses on inference rather than training capabilities. ### Is DeepLearningExamples or ncnn more popular on GitHub? ncnn has more GitHub stars (23,644 vs 14,844). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepLearningExamples and ncnn open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepLearningExamples or ncnn? GraphCanon lists graph-backed alternatives at [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) and [ncnn alternatives](/tools/tencent-ncnn/alternatives) ([DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/alternatives.md), [ncnn markdown twin](/tools/tencent-ncnn/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/nvidia-deeplearningexamples-vs-tencent-ncnn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepLearningExamples or ncnn? DeepLearningExamples: Dormant. ncnn: Very active. 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 DeepLearningExamples and ncnn? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust); [ncnn trust report](/tools/tencent-ncnn/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-deeplearningexamples`](/api/graphcanon/graph?tool=nvidia-deeplearningexamples) - 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/_