vss-evaluate-caption-accuracy skillA
vss-evaluate-caption-accuracy is agent-read markdown (skill) from nvidia-ai-blueprints/video-search-and-summarization: Measure whether an RT-VLM configuration change altered caption quality — capture paired baseline and candidate captions for a set of videos, score both against a ground truth with an LLM judge, and emit an accuracy and processing-time table. Use when changing frame selection, decode, or model settings and you need evidence there is no accuracy regression..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
## Purpose Answer one question with evidence: **did this RT-VLM change make captions worse?** A configuration change that saves processing time is only useful if caption quality holds. This skill captures captions twice over the same videos — once with the change (HYP) and once without (REF) — scores both against a ground truth using an LLM judge, and reports accuracy delta alongside time saved. ## Prerequisites Everything except the judge runs **inside the RT-VLM container**. The container needs a GPU, the model on disk, and the `nvdsframeselector` DeepStream plugin (shipped with DeepStream in the RT-VLM image). | Requirement | Notes | |---|---| | Running RT-VLM container | Default name `rtvi_vlm-$USER`. Start it before running any stage | | Model weights | Set `MODEL_PATH` in the deployment `.env`. Runs here used `Qwen3-VL-32B-Instruct`; any vLLM-compatible VLM works | | `VLM_MODEL_TO_USE=vllm-compatible` | In the deployment `.env` | | Source videos | A directory of `.mp4` files. Point `DEDUP_DIR` at it | | `OPENAI_API_KEY` | In the deployment `.env`. Only needed for the `gt` stage (ground truth is gpt-4.1) | …
Read the whole file at its exact version.
How to install
mdr add nvidia-ai-blueprints/video-search-and-summarization/vss-evaluate-caption-accuracy@v3.2.0mdr add nvidia-ai-blueprints/video-search-and-summarization/vss-evaluate-caption-accuracy@sha256:66f14a7d3195576ePin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_7ccejmigml55dbm6)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v3.2.0 latest | 2026-09-23 | 71f6e8b | 7,965 B | A | view · diff |
| v3.2.0 | 2026-09-14 | 4cd3377 | 7,114 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (7965 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
nvidia-ai-blueprints/video-search-and-summarization · 1,876 stars · license NOASSERTION · pushed 2026-09-24 · branch develop
API
GET https://markdownregistry.com/api/v1/artifacts/art_7ccejmigml55dbm6 GET https://markdownregistry.com/api/v1/resolve?ref=nvidia-ai-blueprints/video-search-and-summarization/vss-evaluate-caption-accuracy GET https://markdownregistry.com/api/v1/blob/66f14a7d3195576eca55f865227e43288afec2867167eba8e284a7dc6c5d9bda
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