菜单
Use when working in examples/sam_cvat, or when setting up / running / debugging the datapipe SAM3→CVAT pre-annotation example, on the cats-n-dogs demo data or on your own images.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when asked to create, scaffold, or build a NEW datapipe pipeline or project (any size — plain ETL/ingest, ML training with metrics, LLM-processing steps, annotation loops, viewers/UI), usually in an empty directory outside the datapipe repo. Also when a user describes a recurring data-processing task ("files keep arriving and I need to...") and no pipeline exists yet.
Use when working in examples/ocr, or running / debugging the OCR→FiftyOne datapipe example (multi-LLM structured OCR on passport/id-doc images).
Use when working in the epoch8 datapipe repo with the examples/* folders, when asked to run or debug any datapipe example pipeline on your own data, or when unsure which example or setup skill applies.
Use when setting up or running examples/detection_tags — a self-contained datapipe detection demo built around tags (per-scenario metrics), split into two parts around a checkpoint (train a baseline, then add a tagged TRAIN batch and retrain, watching the tag metric rise), with a FiftyOne view and injected ground truth (no Label Studio). Also for "add a tagged batch, retrain, watch the tag metric rise" and for rehearsing that retraining demo from a saved checkpoint.
Use when setting up or running examples/e2e_template (image_detection or image_keypoints) — datapipe YOLO/Label Studio pipelines — or debugging their install, stages, training, or FiftyOne output.
Use when working in examples/embedder_fiftyone, or running / debugging the embedder→FiftyOne datapipe example (DINOv2/DINOv3 image embeddings, UMAP, similarity search).
| name | setup-sam-cvat |
| description | Use when working in examples/sam_cvat, or when setting up / running / debugging the datapipe SAM3→CVAT pre-annotation example, on the cats-n-dogs demo data or on your own images. |
This skill = run SAM3→CVAT pre-annotation on YOUR images. The HZMD/cats-n-dogs demo is just a smoke-test; the real goal is your own images — set the knobs below first.
Ask first — don't assume (only the unresolved): demo (cats-n-dogs) or your own data? which Postgres + which database for DB_URL — never point it at an existing DB or drop in a default without confirming; external CVAT ready or provision/point at it? reuse an existing venv / uv env or create a fresh one? GPU >8 GB? surface stage logs or run quiet?
How to work: read the setup, then propose a short plan and get a go-ahead before touching anything. Prepare .env and pause for the user to verify it before running. Run each stage with its logs shown and, after each, say what you did and what changed — don't run the pipeline silently. If a stage fails and the cause isn't clear from the normal logs, re-run it with datapipe --debug … run (or --debug-sql for SQL errors); debug is very verbose, so send it to a file and grep it (e.g. datapipe --debug run > /tmp/dp_debug.log 2>&1; grep -nEi "error|traceback" /tmp/dp_debug.log) rather than dumping it inline.
SAM_TEXT_PROMPT == the CVAT labels (CVAT_BOX_LABEL / CVAT_POLYGON_LABEL) == HF_DATASET_LABEL must all mean the same class — a mismatch runs clean but yields 0 useful results.INPUT_DIR=/path (.jpg/.jpeg/.png; stem → image_id). An existing-but-EMPTY INPUT_DIR yields 0 images (no HF fallback).SAM_SCORE_THRESHOLD (0.5), SAM_MAX_DETECTIONS (10).HF_TOKEN
with gated-repo read access (a non-gated token → download/load failure).DB_URL (not started here; tables auto-create via datapipe db create-all).CVAT_URL, CVAT_USERNAME, CVAT_PASSWORD, optional
CVAT_ORGANIZATION, and CVAT_PROJECT_ID for a project whose label names match CVAT_BOX_LABEL /
CVAT_POLYGON_LABEL.uv + Python ≥3.10,<3.13 → uv sync. pyproject.toml pins cu124 torch and editable local libs
(../../libs/datapipe-*, resolves only inside the monorepo checkout), and builds sam3 from a pinned
git source (needs git, a compiler, nvcc/CUDA_HOME). If uv sync fails, check the sam3 rev in [tool.uv.sources].ingest (local folder or HF-dataset fallback) → sam (text-prompt inference → boxes + mask
polygons) → cvat (upload images + pre-annotations; parse edits back into image__annotations).Skip if you have data. Leave INPUT_DIR unset → HF fallback; shipped .env.example is self-consistent
(a cat / cat_box/cat_mask / HF_DATASET_LABEL=0=cats) → first run on HZMD/cats-n-dogs yields
detections, confirming install/DB/GPU/CVAT.
cp .env.example .env # DB_URL, HF_TOKEN, CVAT_*, CVAT_PROJECT_ID, SAM_TEXT_PROMPT, labels
uv sync && source .venv/bin/activate # else prefix each command with `uv run`
datapipe db create-all && datapipe run
# or by stage: datapipe step --labels stage=sam run
Run from examples/sam_cvat/ (app.py load_dotenv()s before importing config — wrong cwd breaks .env).
SAM_TEXT_PROMPT IS tracked (→ sam_config table) so changing it retriggers inference — but
CVATStep does NOT push new pre-annotations to EXISTING tasks (only on image add/remove/path change).
Treat the prompt as fixed per run; to change it, wipe the old CVAT tasks + datapipe CVAT tables, re-run.
In CVAT fix preannotations, mark the task completed, re-run datapipe run → edits land in
image__annotations. CVAT tables (for wipes): image_batches, cvat_task, cvat_images (not "cvat_files"), cvat_task_sync_table.
.env class misaligned; re-align all three.CVAT_BOX_LABEL/CVAT_POLYGON_LABEL.