Comparison
DeepLearningExamples vs ncnn
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.
Markdown twin · DeepLearningExamples alternatives · ncnn alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | DeepLearningExamples | ncnn |
|---|---|---|
| Maintenance | Dormant (734d since push) As of 1w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- DeepLearningExamples
- State-of-the-Art Deep Learning scripts for various applications
- ncnn
- High-performance neural network inference framework optimized for mobile platforms
Stars
- DeepLearningExamples
- 15k
- ncnn
- 24k
Forks
- DeepLearningExamples
- 3.4k
- ncnn
- 4.5k
Open issues
- DeepLearningExamples
- 321
- ncnn
- 1.2k
Language
- DeepLearningExamples
- Jupyter Notebook
- ncnn
- C++
Adopt for
- DeepLearningExamples
- Curated facts for DeepLearningExamples, tailored to its unique features and offerings.
- ncnn
- 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
- DeepLearningExamples
- -
- ncnn
- -
Runtime
- DeepLearningExamples
- -
- ncnn
- -
License
- DeepLearningExamples
- -
- ncnn
- Other, details not specified within the provided repository content.
Last pushed
- DeepLearningExamples
- Aug 12, 2024
- ncnn
- Aug 4, 2026
Categories
- DeepLearningExamples
- Inference & Serving, Model Training
- ncnn
- Inference & Serving
Trust and health
Maintenance
- DeepLearningExamples
- Dormant (18%)
- ncnn
- Very active (96%)
Days since push
- DeepLearningExamples
- 734d
- ncnn
- 0d
Open issues (now)
- DeepLearningExamples
- 321
- ncnn
- 1.2k
Stars delta
- DeepLearningExamples
- +14 (30d)
- ncnn
- Unknown
Open issues delta
- DeepLearningExamples
- -1 (30d)
- ncnn
- Unknown
Full report
- DeepLearningExamples
- Trust report
- ncnn
- Trust report
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.
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (NVIDIA/DeepLearningExamples) · observed Aug 17, 2026
- GitHub forks (NVIDIA/DeepLearningExamples) · observed Aug 17, 2026
- Last push (NVIDIA/DeepLearningExamples) · observed Aug 12, 2024
- License file (unknown) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Tencent/ncnn) · observed Aug 4, 2026
- GitHub forks (Tencent/ncnn) · observed Aug 4, 2026
- Last push (Tencent/ncnn) · observed Aug 4, 2026
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: DeepLearningExamples 15k · ncnn 24k (synced Aug 17, 2026).
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 and ncnn alternatives (DeepLearningExamples markdown twin, ncnn markdown twin), 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 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; ncnn trust report.