baseline-detector — community baseline-detector, OpenWorld-AI-Image-Detection, community, ide skills

v1.0.0

关于此技能

非常适合与模型检测和融合模块合作的AI代理,需要调整冻结骨架行为和头部深度的指导。 Use when implementing or modifying the CLIP baseline detector, residual or DIRE fusion modules, model config knobs, or detector-side inference contracts in this repo. Do not use for dataset parsing, calibration-only changes, or plotting work.

rilical rilical
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更新于: 3/8/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 5/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Concrete use-case guidance Explicit limitations and caution
Review Score
5/11
Quality Score
38
Canonical Locale
en
Detected Body Locale
en

非常适合与模型检测和融合模块合作的AI代理,需要调整冻结骨架行为和头部深度的指导。 Use when implementing or modifying the CLIP baseline detector, residual or DIRE fusion modules, model config knobs, or detector-side inference contracts in this repo. Do not use for dataset parsing, calibration-only changes, or plotting work.

核心价值

使代理能够通过调整冻结骨架行为、头部深度和可选的残差融合来微调模型检测,利用Python和模块如`clip_detector.py`。

适用 Agent 类型

非常适合与模型检测和融合模块合作的AI代理,需要调整冻结骨架行为和头部深度的指导。

赋予的主要能力 · baseline-detector

调整冻结骨架行为以改善模型检测
优化头部深度以获得更好的融合模块性能
调试检测器配置形状问题

! 使用限制与门槛

  • 仅限于模型检测和融合模块
  • 需要Python环境
  • 不适用于数据集加载、符合逻辑或评估/报告脚本

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The page lacks a strong recommendation layer.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

评审后的下一步

先决定动作,再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”,而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核,以及是否已经进入工作流落地阶段。

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

baseline-detector 是什么?

非常适合与模型检测和融合模块合作的AI代理,需要调整冻结骨架行为和头部深度的指导。 Use when implementing or modifying the CLIP baseline detector, residual or DIRE fusion modules, model config knobs, or detector-side inference contracts in this repo. Do not use for dataset parsing, calibration-only changes, or plotting work.

如何安装 baseline-detector?

运行命令:npx killer-skills add rilical/OpenWorld-AI-Image-Detection/baseline-detector。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

baseline-detector 适用于哪些场景?

典型场景包括:调整冻结骨架行为以改善模型检测、优化头部深度以获得更好的融合模块性能、调试检测器配置形状问题。

baseline-detector 支持哪些 IDE 或 Agent?

该技能兼容 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer。可使用 Killer-Skills CLI 一条命令通用安装。

baseline-detector 有哪些限制?

仅限于模型检测和融合模块;需要Python环境;不适用于数据集加载、符合逻辑或评估/报告脚本。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add rilical/OpenWorld-AI-Image-Detection/baseline-detector。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    baseline-detector 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

baseline-detector

安装 baseline-detector,这是一款面向AI agent workflows and automation的 AI Agent Skill。查看评审结论、使用场景与安装路径。

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Baseline Detector

Use when

  • Working on src/owaid/models/clip_detector.py, fusion modules, or detector config shape.
  • Adjusting frozen-backbone behavior, head depth, or optional residual fusion.

Do not use when

  • The task is purely about dataset loading, conformal logic, or evaluation/reporting scripts.

Workflow

  1. Keep the encoder frozen by default unless config explicitly relaxes that.
  2. Keep model files free of training-loop and dataset-loading logic.
  3. Preserve a clean baseline path when DIRE is disabled.
  4. Maintain a stable output contract for training, eval, and inference callers.

Outputs

  • CPU-safe detector modules with explicit config controls.
  • Optional fusion components that do not contaminate the baseline path.

Success criteria

  • The model emits two-class logits for {Real, AI}.
  • Baseline and fusion paths remain separable and testable.

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