Ai Image Analyzer Pro

F 46 completed
Ai Ml
unknown / python · tiny
38
Files
14,614
LOC
0
Frameworks
4
Languages

Pipeline State

completed
Run ID
#366606
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
41.00
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47727
Member of a group with 1 similar repo(s) — canonical #18123 view group →
Top concepts (2)
Project DescriptionData/ML
Repobility — the code-quality scanner for AI-generated software · https://repobility.com

AI Prompt

Create a universal AI Image Analyzer Pro tool, primarily using Python. This tool needs to evaluate the quality of AI-generated images, specifically for domains like medical and satellite imaging. It should support analyzing images using 18 different metrics, including CLIP, LPIPS, and SSIM, and must have functionality for hallucination detection. I'd like to include features for both single-image analysis and batch processing. Please structure it to handle cross-domain quality assessment and generate detailed statistical reports.
python ai image-analysis computer-vision medical-imaging quality-assessment machine-learning batch-processing
Generated by gemma4:latest

Catalog Information

The AI Image Analyzer Pro is a universal tool for evaluating the quality of AI-generated images, particularly in medical and satellite imaging.

Description

This project provides an AI super-resolution quality evaluation tool with 18 metrics (CLIP, LPIPS, SSIM, PSNR) for cross-domain hallucination detection. It supports various domains such as medical, satellite, microscopy, anime, and more. The tool is GPU-accelerated and generates detailed statistical reports.

الوصف

هذا المشروع يقدم أداة تقييم جودة الصور الناتجة عن الذكاء الاصطناعي، مع 18 مؤشرًا (CLIP، LPIPS، SSIM، PSNR) لdetección de alucinaciones transdominio. يدعم العديد من المجالات مثل الطب، وال卫اقية، وعلم الأحياء الدقيقة، والأنمي، وغيرها. الأداة مصممة للعمل على GPU وتقدم تقارير إحصائية تفصيلية.

Novelty

9/10

Tags

ai-super-resolution quality-assessment cross-domain medical-imaging satellite-imagery microscopy computer-vision

Technologies

huggingface matplotlib numpy pandas plotly pytorch scikit-learn scipy

Claude Models

claude (unknown version)

Quality Score

F
45.9/100
Structure
40
Code Quality
44
Documentation
65
Testing
0
Practices
53
Security
92
Dependencies
60

Strengths

  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • No tests found \u2014 high risk of regressions
  • No CI/CD configuration \u2014 manual testing and deployment
  • 4 bare except/catch blocks swallowing errors
  • 1245 duplicate lines detected \u2014 consider DRY refactoring
  • 6 'god files' with >500 LOC need decomposition

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Replace bare except/catch blocks with specific exception types

Security & Health

7.6h
Tech Debt (B)
A
OWASP (100%)
FAIL
Quality Gate
A
Risk (13)
Repobility — same analyzer, your code, free for public repos · /scan/
Unknown
License
2.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
70.3%
json
18.2%
markdown
9.6%
text
1.9%

Frameworks

None detected

Concepts (2)

Source-of-truth: Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
auto_descriptionProject Description![DOI: v1.7.2](https://doi.org/10.5281/zenodo.17677441) ![DOI: v1.7.1](https://doi.org/10.5281/zenodo.17656774) ![DOI: v1.7.0](https://doi.org/10.5281/zenodo.17645618)80%
auto_categoryData/MLdata-ml70%

Quality Timeline

1 quality score recorded.

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