Leaderboard github.com
rbgirshick/py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
AI provenance
0%
none-detected
- Confidence
- 65%
- Coverage
- 100%
Estimated share of AI-generated code. Explicit attribution is a lower bound.
Code quality
47/100
poor
- Confidence
- 90%
- Coverage
- 100%
Complexity, duplication, lint density, tests, CI, docs and review process.
Security
66/100
weak
- Confidence
- 85%
- Coverage
- 100%
Secrets, static analysis, vulnerable dependencies, CI and supply-chain posture.
- Repository: https://github.com/rbgirshick/py-faster-rcnn
- Commit:
781a917b378dbfdedb45b6a56189a31982da1b43 - Generated: 2026-11-13T23:20:34+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 | 65% | 100% |
| Code quality | 47 | poor | 90% | 100% |
| Security | 66 | weak | 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%
-
Stylometry index: 0.15 per 1,000 lines (narrated steps 1, placeholders 0, assistant phrasing 0, emoji 0). Reported as a signal, not scored.
| Month | Lines added | Attributed to AI |
|---|---|---|
| 2015-02 | 1,268 | 0% |
| 2015-03 | 1,967 | 0% |
| 2015-04 | 2,652 | 0% |
| 2015-05 | 17 | 0% |
| 2015-08 | 2,081 | 0% |
| 2015-10 | 8 | 0% |
| 2015-12 | 51 | 0% |
| 2016-01 | 1 | 0% |
| 2016-02 | 15,483 | 0% |
| 2016-03 | 3 | 0% |
| 2017-04 | 3 | 0% |
Evidence
- step-by-step narration comment: # Finally, a note about the intersection over union (iou) computation. (lib/pycocoto)
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 0.01, no CI test run detected; 15% of function code in complex or long functions; 12% duplicated lines. Weakest area: testing.
| Area | Score |
|---|---|
| Complexity (lizard) | 64 |
| Duplication | 48 |
| Code smells (Opengrep) | 41 |
| Tests and CI | 30 |
| Documentation | 65 |
| Type safety | 40 |
| Process (Scorecard review/maintenance) | n/a |
- Functions: 236; mean complexity 3.5, 90th percentile 7; 8 complex and 1 long functions
- Duplicated lines: 12.4% of 5,340 compared
- Tests: 90 test lines for 6,760 source lines (ratio 0.01); CI test commands: none detected; 23% of recent source commits also change tests
- Docs missing or thin: contributing, changelog
Most complex functions:
- evaluateImg in lib/pycocotools/cocoeval.py: complexity 35, 64 lines
- accumulate in lib/pycocotools/cocoeval.py: complexity 32, 79 lines
- rleFrPoly in lib/pycocotools/maskApi.c: complexity 29, 38 lines
- voc_eval in lib/datasets/voc_eval.py: complexity 22, 85 lines
- getAnnIds in lib/pycocotools/coco.py: complexity 18, 18 lines
Security
Security posture is weak: 5 high-severity SAST findings. Weakest area: posture.
| Area | Score |
|---|---|
| Secrets (gitleaks, tree + history) | 100 |
| Static analysis (Opengrep) | 57 |
| 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
Top findings
| Severity | Finding | Location |
|---|---|---|
| high | opengrep/python.lang.security.use-defused-xml: The Python documentation recommends using defusedxml instead of xml because the native Python xml library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service. [confidence low] |
lib/datasets/pascal_voc.py:11 |
| high | opengrep/python.lang.security.use-defused-xml-parse: The native Python xml library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service. Do not use this library to parse untrusted input. Instead the Python documentation recommends using defusedxml. [confidence medium] |
lib/datasets/pascal_voc.py:186 |
| high | opengrep/python.lang.security.audit.dangerous-subprocess-use-audit: Detected subprocess function 'call' without a static string. If this data can be controlled by a malicious actor, it may be an instance of command injection. Audit the use of this call to ensure it is not controllable by an external resource. You may consider using 'shlex.escape()'. [confidence low] |
lib/datasets/pascal_voc.py:318 |
| high | opengrep/python.lang.security.use-defused-xml: The Python documentation recommends using defusedxml instead of xml because the native Python xml library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service. [confidence low] |
lib/datasets/voc_eval.py:7 |
| high | opengrep/python.lang.security.use-defused-xml-parse: The native Python xml library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service. Do not use this library to parse untrusted input. Instead the Python documentation recommends using defusedxml. [confidence medium] |
lib/datasets/voc_eval.py:14 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/coco.py:143 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/coco.py:157 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/coco.py:216 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/coco.py:224 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/coco.py:333 |
| medium | opengrep/python.lang.security.audit.eval-detected: Detected the use of eval(). eval() can be dangerous if used to evaluate dynamic content. If this content can be input from outside the program, this may be a code injection vulnerability. Ensure evaluated content is not definable by external sources. [confidence low] |
lib/datasets/imdb.py:55 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/pascal_voc.py:102 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/pascal_voc.py:109 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/pascal_voc.py:126 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/pascal_voc.py:137 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/pascal_voc.py:158 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/pascal_voc.py:289 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/voc_eval.py:116 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/datasets/voc_eval.py:120 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
lib/fast_rcnn/test.py:292 |
| medium | opengrep/python.lang.security.audit.dynamic-urllib-use-detected: Detected a dynamic value being used with urllib. urllib supports 'file://' schemes, so a dynamic value controlled by a malicious actor may allow them to read arbitrary files. Audit uses of urllib calls to ensure user data cannot control the URLs, or consider using the 'requests' library instead. [confidence low] |
lib/pycocotools/coco.py:350 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
tools/reval.py:50 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
tools/rpn_generate.py:90 |
| medium | opengrep/python.lang.security.deserialization.avoid-cPickle: Avoid using cPickle, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. [confidence low] |
tools/train_faster_rcnn_alt_opt.py:169 |
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.
Distribution signals
- readme-download-lure: links to file hosts or shorteners: dropbox.com/s
Signals consistent with a repository that exists to distribute a download. They are heuristics, not a verdict; release files were not downloaded or inspected.
Repository profile
- Source lines: 6,760; test lines: 90
| Language | Lines |
|---|---|
| Python | 6,210 |
| Shell | 295 |
| C | 263 |
| Objective-C | 80 |
| C++ | 2 |