Comparison
keras vs ncnn
Verdict
Pick keras when keras is primarily Python; ncnn is C++; pick ncnn when ncnn is primarily C++; keras is Python.
Markdown twin · keras alternatives · ncnn alternatives
GraphCanon updated today
Trust & integrity
| Signal | keras | ncnn |
|---|---|---|
| Maintenance | Very active (4d since push) As of today · github_public_v1 | Very active (3d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No criticals As of today · osv@v1 | No lockfile As of today · none |
Tagline
- keras
- Deep Learning for humans
- ncnn
- ncnn is a high-performance neural network inference framework optimized for the mobile platform
Stars
- keras
- 64k
- ncnn
- 24k
Forks
- keras
- 20k
- ncnn
- 4.5k
Open issues
- keras
- 228
- ncnn
- 1.2k
Language
- keras
- Python
- ncnn
- C++
Adopt for
- keras
- -
- ncnn
- -
Persona
- keras
- -
- ncnn
- -
Runtime
- keras
- -
- ncnn
- -
License
- keras
- Apache-2.0
- ncnn
- Other
Last pushed
- keras
- Jul 7, 2026
- ncnn
- Jul 8, 2026
Categories
- keras
- Model Training
- ncnn
- Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Days since push
- keras
- 4d
- ncnn
- 3d
Open issues (now)
- keras
- 228
- ncnn
- 1.2k
Security scan
- keras
- No criticals
- ncnn
- No lockfile
Full report
- keras
- Trust report
- ncnn
- Trust report
Shared compatibility
- Python · keras: Python runtime · ncnn: Python runtime
Choose keras if…
- keras is primarily Python; ncnn is C++.
- License: keras is Apache-2.0, ncnn is Other.
- Tags unique to keras: data-science, jax, machine-learning, neural-networks.
When NOT to use keras
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Choose ncnn if…
- ncnn is primarily C++; keras is Python.
- License: ncnn is Other, keras is Apache-2.0.
- Tags unique to ncnn: android, arm-neon, artificial-intelligence, caffe.
- Also covers Evaluation & Observability, Inference & Serving.
When NOT to use ncnn
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (keras-team/keras) · observed Jul 11, 2026
- GitHub forks (keras-team/keras) · observed Jul 11, 2026
- Last push (keras-team/keras) · observed Jul 7, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Tencent/ncnn) · observed Jul 11, 2026
- GitHub forks (Tencent/ncnn) · observed Jul 11, 2026
- Last push (Tencent/ncnn) · observed Jul 8, 2026
- License file (Other) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: keras 64k · ncnn 24k (synced Jul 11, 2026).
Common questions
- What is the difference between keras and ncnn?
- keras: Deep Learning for humans. ncnn: ncnn is a high-performance neural network inference framework optimized for the mobile platform. See the comparison table for live GitHub stats and shared categories.
- When should I choose keras over ncnn?
- Choose keras over ncnn when keras is primarily Python; ncnn is C++; License: keras is Apache-2.0, ncnn is Other; Tags unique to keras: data-science, jax, machine-learning, neural-networks.
- When should I choose ncnn over keras?
- Choose ncnn over keras when ncnn is primarily C++; keras is Python; License: ncnn is Other, keras is Apache-2.0; Tags unique to ncnn: android, arm-neon, artificial-intelligence, caffe; Also covers Evaluation & Observability, Inference & Serving.
- When should I avoid keras?
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- When should I avoid ncnn?
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is keras or ncnn more popular on GitHub?
- keras has more GitHub stars (64,191 vs 23,520). Stars measure visibility, not whether either tool fits your constraints.
- Are keras and ncnn open source?
- Yes - both are open-source projects on GitHub (keras: Apache-2.0, ncnn: Other).
- Where can I find alternatives to keras or ncnn?
- GraphCanon lists graph-backed alternatives at keras alternatives and ncnn alternatives (keras 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, keras or ncnn?
- keras: Very active. 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 keras and ncnn?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: keras trust report; ncnn trust report.