Add images for curb detection and semantic segmentation proposals

[NishantSinghhhhh]

What type of PR is this?

/kind bug
/kind documentation
/kind cleanup

What this PR does / why we need it:

This PR restores four broken Ianvs examples that are currently non-functional
due to dependency evolution, API breakage, and version incompatibilities:

1. Cityscapes-Synthia Lifelong Learning — Curb Detection: Fixes 12
   confirmed bugs spanning example code, Sedna core (core/lib/sedna/),
   and the Ianvs paradigm core controller. The lifelong learning paradigm
   is entirely non-functional without these fixes.

2. Cityscapes-Synthia Lifelong Learning — Semantic Segmentation: Fixes
   15 confirmed bugs across the same three layers, completely restoring the
   evaluation phase which was previously unable to run.

3. LLM-Agent: Adds missing requirements.txt, fixes configuration path
   mismatches, resolves dataset schema inconsistencies, automates model
   download, and rewrites the README. Reduces new user setup time from 5+
   hours to under 30 minutes.

4. LLM-Edge-Benchmark-Suite: Refactors basemodel.py to support
   configurable model IDs via hyperparameters, adds per-model algorithm
   YAML configurations, and updates the benchmarking job to enable
   multi-algorithm comparison.

Additionally, this PR establishes a CI/CD pipeline via GitHub Actions for
automated testing of all four restored examples across Python 3.8, 3.9,
and 3.10, with failure detection and clear feedback mechanisms for
contributors.

Which issue(s) this PR fixes:

Fixes #230

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This pull-request has been approved by: NishantSinghhhhh
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[NishantSinghhhhh]

@MooreZheng sir I have Added the complete Proposal for LFX Term-1 2026

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses critical usability issues within the KubeEdge Ianvs project by comprehensively restoring four previously non-functional examples: Cityscapes-Synthia Lifelong Learning (Curb Detection and Semantic Segmentation), LLM-Agent, and LLM-Edge-Benchmark-Suite. The changes involve fixing numerous bugs across multiple layers of the codebase, enhancing documentation, and streamlining user setup processes. Furthermore, a new CI/CD pipeline has been established to automate validation of these examples, ensuring their ongoing stability and improving the overall contributor experience.

Highlights

• Curb Detection Example Restored: Fixed 12 bugs in the Cityscapes-Synthia Lifelong Learning — Curb Detection example across example code, Sedna core, and Ianvs paradigm core controller, ensuring end-to-end execution.
• Semantic Segmentation Example Restored: Fixed 15 bugs in the Cityscapes-Synthia Lifelong Learning — Semantic Segmentation example across three layers, completely restoring its evaluation phase.
• LLM-Agent Example Enhanced: Added missing requirements.txt, resolved configuration path mismatches and dataset schema inconsistencies, automated model download, and rewrote the README, reducing new user setup time from 5+ hours to under 30 minutes.
• LLM-Edge-Benchmark-Suite Improved: Refactored basemodel.py to support configurable model IDs via hyperparameters, added per-model algorithm YAML configurations, and updated the benchmarking job to enable multi-algorithm comparison.
• CI/CD Pipeline Established: Implemented GitHub Actions for automated testing of all four restored examples across Python 3.8, 3.9, and 3.10, including failure detection and feedback mechanisms.

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Changelog

• docs/proposals/scenarios/Example_Restoration/Example_Restoration.md
  • Added a detailed proposal for the restoration of four Ianvs examples, outlining the background, goals, scope, design, roadmap, and success metrics.

Activity

• The author has been actively working on restoring Ianvs examples since December 2025, documenting errors and resolutions in a dedicated repository, with progress verifiable through commit history since December 24, 2025.

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[gemini-code-assist]

Code Review

This pull request introduces a comprehensive proposal for restoring and validating Ianvs examples, including fixing numerous bugs, improving documentation, and establishing a CI/CD pipeline. The detailed breakdown of goals, scope, design, and roadmap provides excellent clarity on the planned work. However, there are a couple of minor inaccuracies regarding dates and a missing newline at the end of the markdown file that should be addressed for improved accuracy and adherence to best practices.

_{Note: Security Review has been skipped due to the limited scope of the PR.}