label

Labeling Modes

Set label_mode in config.json:

Mode	How it works	Best for
cua+sam	CUA clicks on objects → SAM segments precise boundaries	Best accuracy, hackathon demo
gemini	Gemini native bounding box detection (0-1000 scale)	Fast, good native bbox support
gpt	GPT vision model returns JSON bounding boxes	Simple fallback
codex	Codex subagents view images and write YOLO labels directly	No API keys

Instructions

1. Read config.json for label_mode, classes, model, num_agents If the user asks to call subagent, route to parallel dispatch in step 5.

2. CUA+SAM mode (recommended): Run: uv run .agents/skills/label/scripts/label_cua_sam.py Requires: OPENAI_API_KEY, classes must be set in config.json

3. Gemini mode: Run: uv run .agents/skills/label/scripts/label_gemini.py Requires: GEMINI_API_KEY or GOOGLE_API_KEY

4. GPT mode (fallback): Run: uv run .agents/skills/label/scripts/run.py Requires: OPENAI_API_KEY

5. Parallel dispatch (GPT or Codex mode): Run: bash .agents/skills/label/scripts/dispatch.sh [num_agents] Creates N git worktrees, dispatches N Codex subagents, merges results. If Codex subagents are unavailable in-session, this shell command is the fallback path. Supports:

   • label_mode=gpt with OPENAI_API_KEY (runs run_batch.py)
   • label_mode=codex without API keys (Codex image-viewing subagents)

6. Outputs: output/frames/*.txt (YOLO labels), output/classes.txt

Scripts

Script	Mode	Description
label_cua_sam.py	cua+sam	CUA for clicks + SAM for segmentation
label_gemini.py	gemini	Gemini native bounding boxes
run.py	gpt	GPT vision structured output
run_batch.py	gpt	GPT vision (subagent batch mode)
dispatch.sh	gpt/codex	Parallel subagent orchestrator
merge_classes.py	all	Unify class maps from subagents
auto_label_and_show.py	all	Auto-run configured labeler and print/render label previews