fchollet/deep-learning-models
Keras code and weights files for popular deep learning models.
- Repository: https://github.com/fchollet/deep-learning-models
- Commit:
ccd0eb24996b4cbff4231b90cd44b057c0b20f14 - Generated: 2026-11-13T23:20:46+00:00
- Scorer 0.1.0, scoring prov-4+qual-2+sec-2, report schema v2
Scores
| Pillar | Score | Signal | Confidence | Coverage |
|---|---|---|---|---|
| AI provenance (estimated AI-generated share) | 0 | none-detected | 44% | 100% |
| Code quality | 30 | poor | 90% | 100% |
| Security | 82 | adequate | 85% | 100% |
AI provenance
No AI attribution or AI-consistent velocity signals detected.
- Explicitly attributed share (lower bound): 0.0%
- Unattributed share in AI-consistent velocity bursts: 0.0%
- Estimated AI-generated share: 0.0%
| Month | Lines added | Attributed to AI |
|---|---|---|
| 2016-08 | 980 | 0% |
| 2016-10 | 485 | 0% |
| 2017-03 | 701 | 0% |
| 2017-06 | 709 | 0% |
| 2017-09 | 400 | 0% |
AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.
Code quality
Code quality is poor: test-to-source ratio 0.00, no CI test run detected; 66% of function code in complex or long functions; 34% duplicated lines. Weakest area: testing.
| Area | Score |
|---|---|
| Complexity (lizard) | 14 |
| Duplication | 13 |
| Code smells (Opengrep) | 91 |
| Tests and CI | 0 |
| Documentation | 65 |
| Type safety | 40 |
| Process (Scorecard review/maintenance) | n/a |
- Functions: 30; mean complexity 7.0, 90th percentile 18; 7 complex and 4 long functions
- Duplicated lines: 33.7% of 2,149 compared
- Tests: 0 test lines for 2,764 source lines (ratio 0.00); CI test commands: none detected; 0% of recent source commits also change tests
- Docs missing or thin: contributing, changelog
Most complex functions:
- MobileNet in mobilenet.py: complexity 28, 133 lines
- InceptionResNetV2 in inception_resnet_v2.py: complexity 20, 123 lines
- InceptionV3 in inception_v3.py: complexity 18, 196 lines
- ResNet50 in resnet50.py: complexity 18, 92 lines
- VGG16 in vgg16.py: complexity 17, 84 lines
Security
Security posture is adequate: no high-impact findings. Weakest area: posture.
| Area | Score |
|---|---|
| Secrets (gitleaks, tree + history) | 100 |
| Static analysis (Opengrep) | 100 |
| Vulnerable dependencies (OSV) | n/a |
| CI workflows (zizmor) | n/a |
| Posture (policy, updates, signing, lockfiles) | 0 |
| OpenSSF Scorecard | n/a |
- Posture checks passed: lockfiles; missing: security policy, dependency updates, signed commits
Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.
Repository profile
- Source lines: 2,764; test lines: 0
| Language | Lines |
|---|---|
| Python | 2,764 |