nemotron-page-elements-v3
by nvidia
Lightweight document layout detector for tables, charts, and text regions
nvidia/nemotron-page-elements-v3mixpeek://document_extractor@v1/nvidia_nemotron_page_elements_v3Deploy nemotron-page-elements-v3
Single-tenantMixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.
Overview
Nemotron Page Elements v3 is NVIDIA's compact 54M parameter document layout detection model built on the YOLOX architecture. It identifies six categories of document elements -- tables, charts, infographics, titles, text blocks, and headers/footers -- on 1024x1024 input images. Purpose-built for enterprise document RAG pipelines where layout detection is a preprocessing step before OCR or structured extraction.
Architecture
YOLOX anchor-free object detector with a DarkNet53 backbone and Feature Pyramid Network (FPN). Processes document page images at 1024x1024 resolution and outputs bounding boxes with class labels for six element types. The anchor-free design simplifies deployment and improves detection of elements with unusual aspect ratios (wide tables, tall infographics).
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so nemotron-page-elements-v3 runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
"https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
{
method: "POST",
headers: {
Authorization: "Bearer API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
collection_id: "col_your_collection",
documents: [
{
document_id: "asset-00412",
// Boxes, masks, depth maps and anomaly scores are structured
// results, not vectors. They go in payload and are reachable
// through pre_filters on a retriever, not through similarity.
payload: {
detections: modelOutput,
source_key: "archive/2026/asset-00412",
},
},
],
}),
},
);
// No managed alternative for an open label set. Two extractors do emit a
// bbox, for the one thing each detects: document_graph_extractor@v1 per
// layout block, face_identity_extractor@v1 per face. Nothing ships that
// returns masks, depth maps or anomaly scores.Capabilities
- Table region detection
- Chart and infographic localization
- Title and header identification
- Text block segmentation
- Page layout analysis
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| DocLayNet | [email protected] | 78.4 | Model card |
| PubLayNet | [email protected] | 94.1 | Model card |
| Internal (6-class) | [email protected] | 91.7 | Model card |
Performance
Common Pipeline Companions
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Specification
Research Paper
Model paper or technical report
arxiv.orgBuild a pipeline with nemotron-page-elements-v3
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