lillyguisnet/sam3-segmentation

选用指南

AI 根据 README 整理 · 2026/10/25 01:26:11

适用场景

适用于需要自动化分割照片中物体（如玉米粒、种子、昆虫）并生成可审查掩码的场景，支持多物体计数和导出。

查看 README 依据

Ask your coding agent (Claude Code, Codex, Cursor, Pi, OpenCode and about 75 others) to "outline every kernel", "cut out the dog" or "count the seeds", and this skill gives it the model, the commands and the rules to do it properly: - **Quick calls:** every instance of a phrase ("all the jars"), more objects like an example box, one object from a click or a box, or "segment everything". - **A careful workflow** for many small objects or parts (kernels, seeds, cells, insects): overlapping close-up crops, every raw candidate saved, score checks,

review sheets and flags, recorded and reversible review decisions, boundary refinement, PNG and COCO RLE exports, and a zoomable viewer. - **Honest output:** masks are reported as model proposals, never as verified ground truth, with what is uncertain listed. ## Install Pick one. Each installs the same skill folder. ```sh # Any agent (Claude Code, Codex, Cursor, OpenCode, Gemini CLI, ...). Needs Node.js 22+. npx skills add lillyguisnet/sam3-segmentation # add -g for every project # Claude Code plugin, with updates (Claude Code 2.1.275 or later):

使用方式

通过编码代理以自然语言请求使用，例如“outline every kernel”，技能会自动加载。也可通过npx或插件方式安装。

查看 README 依据

Ask your coding agent (Claude Code, Codex, Cursor, Pi, OpenCode and about 75 others) to "outline every kernel", "cut out the dog" or "count the seeds", and this skill gives it the model, the commands and the rules to do it properly: - **Quick calls:** every instance of a phrase ("all the jars"), more objects like an example box, one object from a click or a box, or "segment everything". - **A careful workflow** for many small objects or parts (kernels, seeds, cells, insects): overlapping close-up crops, every raw candidate saved, score checks,

/plugin install sam3-segmentation --marketplace lillyguisnet/sam3-segmentation # No Node.js: copy the folder yourself (--target claude, codex or pi) git clone https://github.com/lillyguisnet/sam3-segmentation python3 sam3-segmentation/install.py --target claude ``` Then ask your agent in plain words. The skill loads by itself when the request fits; you can also name it (`/sam3-segmentation` in Claude Code, where a plugin install shows it as `/sam3-segmentation:sam3-segmentation`, or `$sam3-segmentation` in Codex). For example:

部署要求

需要Linux（或Windows WSL2）系统、NVIDIA GPU（建议12-24 GB显存）、uv工具、约11 GB磁盘空间，以及Hugging Face上对facebook/sam3模型的访问权限。

查看 README 依据

> Outline every visible kernel in corn.jpg, and the whole ears separately. Keep > overlapping levels, check every region, and tell me what is uncertain. Ask me > before any large download. ## What you need | To... | You need | |---|---| | Run the model | Linux (or Windows WSL2) with an **NVIDIA GPU** (12-24 GB memory suggested), `uv`, about **11 GB** of disk, and your own accepted access to [facebook/sam3](https://huggingface.co/facebook/sam3) on Hugging Face |

已知限制

输出为模型提议，非人工验证；不适用于视频、医学图像或图像分类编辑；小物体需完整工作流，快速调用可能遗漏。

查看 README 依据

token, and stated the 11 GB download before asking to install; without it they mostly did not. [Evals and full results](evals/README.md). ## Limitations - Outputs are proposals. To claim accuracy, compare against masks a person has checked, never against these drafts. - The skill does not yet say when a review is finished, which reviewer to use, or stop an agent from removing many proposals unseen. See the [changelog](CHANGELOG.md). - Small objects need the full workflow; a quick call on a whole photo can miss them entirely.

- Not for video, medical images, or classifying and editing pictures. ## Documentation - [The agent's instructions](skills/sam3-segmentation/SKILL.md) - [Quick SAM calls](skills/sam3-segmentation/references/direct-sam.md) - [Setup and troubleshooting](skills/sam3-segmentation/references/setup.md) - [Method and coordinates](skills/sam3-segmentation/references/method.md) - [Configuration, budgets and run files](skills/sam3-segmentation/references/configuration.md) - [Review protocol](skills/sam3-segmentation/references/review.md)