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38:["$","$L3b",null,{"hasPartnerEndpoint":false}] 41:T1b2b,# **nemotron-page-elements-v3** ## **Description** nemotron-page-elements-v3 is a specialized object detection model designed to identify and extract key page elements in documents, including tables, charts, infographics, titles, header/footers, and text regions. It supports document analysis and multimodal extraction workflows used in enterprise document understanding and retrieval. *This model is ready for commercial/non-commercial use.* ## **License and Terms of Use:** **GOVERNING TERMS:** The trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). **You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.** **Model Developer:** NVIDIA ## **Deployment Geography:** **Global** ## **Use Case:** This model is designed for automating extraction of page elements in enterprise documents, including: - Enterprise document extraction, embedding, and indexing - Augmenting Retrieval Augmented Generation (RAG) workflows with multimodal retrieval - Data extraction from legacy documents and reports This model supersedes the [nemoretriever-page-elements-v2 model](https://build.nvidia.com/nvidia/nemoretriever-page-elements-v2). ## **Release Date:** Build.NVIDIA.com 03/02/2026 via [nemotron-page-elements-v3](https://build.nvidia.com/nvidia/nemotron-page-elements-v3)
## **Reference(s):** **References:** - [YOLOX Paper](https://arxiv.org/abs/2107.08430) - [YOLOX Repository](https://github.com/Megvii-BaseDetection/YOLOX) - [Approaches to PDF Data Extraction for Information Retrieval](https://developer.nvidia.com/blog/approaches-to-pdf-data-extraction-for-information-retrieval/) ## **Model Architecture:** **Architecture Type:** YOLOX
**Network Architecture:** DarkNet53 Backbone + FPN decoupled head (one 1x1 convolution + 2 parallel 3x3 convolutions: one for classification and one for bounding box prediction)
**Number of Model Parameters:** ~5.4e7
**Input Resize:** (1024, 1024) ### **Input:** **Input Types:** Image
**Input Formats:** RGB
**Input Parameters:** Two Dimensional (2D)
**Other Input Properties:** Image is resized to (1024, 1024). ### **Output:** **Output Types:** Structured detections (bounding boxes + labels + confidence)
**Output Format:** JSON-compatible structure
**Output Parameters:** One Dimensional (1D)
**Other Output Properties:** Outputs bounding boxes, confidence scores, and object classes (chart, table, infographic, title, text, header/footer). Thresholds used for non-maximum suppression: `conf_thresh = 0.01`; `iou_thresh = 0.5`.

**Output Classes:** * **Table**: Data structured in rows and columns * **Chart**: Specifically bar charts, line charts, or pie charts * **Infographic**: Visual representations of information more complex than a chart (diagrams, flowcharts); maps are not considered infographics * **Title**: Section titles, or table/chart/infographic titles * **Header/footer**: Page headers and footers * **Text**: Regions of one or more text paragraphs, or standalone text not belonging to any of the classes above __Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.__ ## **Software Integration:** **Runtime Engines:** TensorRT
**Supported Hardware Microarchitecture Compatibility:**
NVIDIA Ampere
NVIDIA Hopper
NVIDIA Lovelace
**Operating Systems:** Linux __The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__ ## **Model Version(s)** nemotron-page-elements-v3 Short Name: `nemotron-page-elements-v3` ## **Training and Evaluation Datasets:** ### **Training Dataset** **Data Modality:** Image
**Image Training Data Size:** Less than a Million Images
**Training Data Collection:** Automated
**Training Labeling:** Hybrid (Automated, Human)
**Training Properties:** Pretrained on 118,287 images from [COCO train2017](https://cocodataset.org/#download) and fine-tuned on 36,093 images from the [Digital Corpora dataset](https://digitalcorpora.org/), with annotations from [Azure AI Document Intelligence](https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence) and a data annotation team. Bounding boxes per class: 35,328 tables, 44,178 titles, 11,313 charts, 6,500 infographics, 90,812 texts, and 10,743 header/footers. The layout model of Document Intelligence was used with `2024-02-29-preview` API version. ### **Evaluation Dataset** **Evaluation Data Collection:** Hybrid (Automated, Human)
**Evaluation Labeling:** Hybrid (Automated, Human)
**Evaluation Properties:** The primary evaluation set is a cut of Azure labels and Digital Corpora images. Bounding boxes per class: 1,985 tables, 2,922 titles, 498 charts, 572 infographics, 4,400 texts, and 492 header/footers. Mean Average Precision (mAP) was used as an evaluation metric. We evaluated with Azure labels from manually selected pages, as well as manual inspection on public PDFs and PowerPoint slides.

**Per-class Performance Metrics:** | Class | AP (%) | AR (%) | |:--|--:|--:| | table | 44.643 | 62.242 | | chart | 54.191 | 77.557 | | title | 38.529 | 56.315 | | infographic | 66.863 | 69.306 | | text | 45.418 | 73.017 | | header_footer | 53.895 | 75.670 | ## **Inference** **Acceleration Engine:** TensorRT
**Test Hardware:** NVIDIA Hopper (H100 PCIe/SXM) ## **Ethical Considerations** NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case, and address unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).42:T7a5,| Field | Response | | ----- | ----- | | Intended Task/Domain: | Document Understanding | | Model Type: | YOLOX Object Detection for Charts, Tables, Infographics, Header/footers, Texts, and Titles | | Intended User: | Enterprise developers, data scientists, and other technical users who need to extract structural elements from documents. | | Output: | A List of dictionaries containing lists of dictionaries of floating point numbers (representing bounding box information).
**Example**: `{\"data\": [{\"index\": 0,\"bounding_boxes\": {\"table\": [{\"x_min\": 0.6503,\"y_min\": 0.2161,\"x_max\": 0.7835,\"y_max\": 0.3236,\"confidence\": 0.9306}]}}]}` | | Describe how the model works: | The model identifies objects in an image by first dividing the image into a grid. For each grid cell, it extracts visual features and simultaneously predicts which objects are present (for example, 'chart' or 'table') and where they are located in that cell, all in a single pass through the image. | | Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable | | Technical Limitations & Mitigation: | The model may not generalize to unknown document types/formats not commonly found on the web. Further fine-tuning might be required for such documents. | | Verified to have met prescribed NVIDIA quality standards: | Yes | | Performance Metrics: | Mean Average Precision, detectionr recall and visual inspection | | Potential Known Risks: | This model may not always detect all elements in a document. | | Licensing & Terms of Use: | **GOVERNING TERMS:** The trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). |43:T22c93,{ "input": [ { "type": "image_url", "url": 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} ] } 46:T6e5,{ "data": [ { "index": 0, "bounding_boxes": { "table": [ { "x_min": 0.2129, "y_min": 0.1534, "x_max": 0.9212, "y_max": 0.4086, "confidence": 0.8844 } ], "chart": [ { "x_min": 0.2739, "y_min": 0.4891, "x_max": 0.7358, "y_max": 0.7404, "confidence": 0.7354 } ], "title": [ { "x_min": 0.2147, "y_min": 0.4277, "x_max": 0.4197, "y_max": 0.4434, "confidence": 0.8099 }, { "x_min": 0.2155, "y_min": 0.7575, "x_max": 0.3797, "y_max": 0.7757, "confidence": 0.7908 }, { "x_min": 0.432, "y_min": 0.4882, "x_max": 0.6105, "y_max": 0.5127, "confidence": 0.6173 }, { "x_min": 0.362, "y_min": 0.1557, "x_max": 0.6351, "y_max": 0.1727, "confidence": 0.5916 } ], "paragraph": [ { "x_min": 0.2129, "y_min": 0.7809, "x_max": 0.7854, "y_max": 0.8109, "confidence": 0.8977 }, { "x_min": 0.211, "y_min": 0.3866, "x_max": 0.9124, "y_max": 0.4821, "confidence": 0.8204 }, { "x_min": 0.2347, "y_min": 0.387, "x_max": 0.8988, "y_max": 0.4031, "confidence": 0.2735 } ] } } ] } 47:T6c0,{ "data": [ { "index": 0, "bounding_boxes": { "table": [ { "x_min": 0.5039, "y_min": 0.2337, "x_max": 0.6372, "y_max": 0.3537, "confidence": 0.6567 }, { "x_min": 0.649, "y_min": 0.1948, "x_max": 0.784, "y_max": 0.3247, "confidence": 0.6055 }, { "x_min": 0.3592, "y_min": 0.2659, "x_max": 0.4919, "y_max": 0.3884, "confidence": 0.54 }, { "x_min": 0.2155, "y_min": 0.1212, "x_max": 0.7949, "y_max": 0.4579, "confidence": 0.3424 }, { "x_min": 0.2157, "y_min": 0.3179, "x_max": 0.3489, "y_max": 0.436, "confidence": 0.3215 }, { "x_min": 0.2436, "y_min": 0.8008, "x_max": 0.7712, "y_max": 0.8378, "confidence": 0.2781 } ], "chart": [ { "x_min": 0.2057, "y_min": 0.551, "x_max": 0.7952, "y_max": 0.8474, "confidence": 0.8485 } ], "title": [ { "x_min": 0.2404, "y_min": 0.1369, "x_max": 0.7238, "y_max": 0.1922, "confidence": 0.6551 }, { "x_min": 0.399, "y_min": 0.566, "x_max": 0.6511, "y_max": 0.5913, "confidence": 0.4216 } ] } } ] } 48:T5c0,## Step 1. Generate API Key ::generate-api-key ## Step 2. Pull and Run the NIM ```bash $ docker login nvcr.io Username: $oauthtoken Password: ``` Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure. ```bash export NGC_API_KEY= export LOCAL_NIM_CACHE=~/.cache/nim mkdir -p "$LOCAL_NIM_CACHE" docker run -it --rm \ --gpus all \ --shm-size=16GB \ -e NGC_API_KEY=$NGC_API_KEY \ -e TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \ -u $(id -u) \ -p 8000:8000 \ nvcr.io/nim/nvidia/nemotron-page-elements-v3:latest ``` ## Step 3. Test the NIM You can now make a local API call using this curl command: ```bash HOSTNAME="localhost" SERVICE_PORT=8000 curl -X "POST" \ "http://${HOSTNAME}:${SERVICE_PORT}/v1/infer" \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "input": [ { "type": "image_url", "url": "data:image/png;base64," }, { "type": "image_url", "url": "data:image/png;base64," } ] }' ``` For more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/ingestion/object-detection/latest/getting-started.html#get-started-with-nvidia-nim-for-object-detection).39:[["$","div",".3",{"className":"relative","children":[["$","div",null,{"className":"relative flex min-h-[194px] w-full flex-col overflow-hidden p-md sm:p-ml bg-manitoulinLightBlack","children":[["$","$L3c",null,{"alt":"","className":"object-cover object-center !left-1/2 z-10 !w-1/2 brightness-20 lg:brightness-75","fill":true,"priority":true,"src":"https://assets.ngc.nvidia.com/products/api-catalog/images/nemotron-page-elements-v3.jpg","style":{"maskImage":"linear-gradient(to left, rgba(0,0,0,1), rgba(0,0,0,0))"}}],["$","div",null,{"className":"z-30 mx-auto flex w-full max-w-screen-xl min-w-0 flex-1 flex-col items-start justify-between gap-md lg:flex-row lg:gap-ml","children":["$","section",null,{"aria-describedby":"resource-description","aria-labelledby":"resource-name","className":"flex w-full min-w-0 flex-col lg:flex-1","children":["$","div",null,{"className":"flex flex-col gap-1.5 whitespace-pre-wrap","children":[["$","$L3d",null,{"className":"w-fit text-ml text-n300 lowercase hover:text-n050","href":"/nvidia","children":"NVIDIA"}],["$","div",null,{"className":"flex flex-col items-start gap-3 lg:flex-row lg:items-center","children":[["$","h1",null,{"className":"max-w-full text-lg leading-heading font-medium break-words text-primary","data-testid":"resource-name","id":"resource-name","children":"nemotron-page-elements-v3"}],[["$","span","AVAILABLE",{"className":"nv-badge nv-badge--kind-solid h-[18px] py-0","data-testid":"nv-badge","ref":"$undefined","children":["$undefined","Downloadable"]}]],"$undefined"]}],["$","p",null,{"className":"max-w-[85ch] text-ms text-primary","id":"resource-description","children":"Model for object detection, fine-tuned to detect charts, tables, and titles in documents."}],["$","div",null,{"className":"flex w-full flex-col items-start gap-md lg:flex-row lg:items-end lg:justify-between","children":[["$","div",null,{"className":"flex w-full min-w-0 flex-wrap gap-sm lg:flex-1","children":["$","$L3e",null,{"artifactType":"ENDPOINT","density":"standard","labels":["Chart Detection","Object Detection","Table Detection","data ingestion","nimType:endpoint:nim_type_run_anywhere","usecase:endpoint:usecase_object_detect"],"searchRoutePath":"/models"}]}],["$","div",null,{"className":"z-30 flex w-full flex-row justify-start gap-ms sm:justify-end lg:w-auto lg:shrink-0","children":["$undefined",["$","$L3f",null,{"cta":{"text":"Apply to Self-Host","url":"https://www.nvidia.com/en-us/ai/nim-notifyme/"},"isNIMAccessRestricted":"$undefined","isNIMAvailable":true}]]}]]}]]}]}]}],["$","div",null,{"className":"pointer-events-none absolute right-0 bottom-0 left-0 z-20 h-30 bg-gradient-to-t to-transparent from-manitoulinLightBlack via-manitoulinLightBlack/70"}]]}],["$","div",null,{"className":"px-4 mx-auto flex max-w-screen-xl flex-col pt-0 lg:px-0","data-body-background":"light-black","children":["$","section",null,{"className":"flex w-full flex-col justify-between rounded-4 border-manitoulinBorderColor bg-manitoulinDarkBlack sm:rounded-6 lg:rounded-8 p-ms lg:p-lg","children":["$","$L40",null,{"artifact":{"artifactType":"ENDPOINT","name":"nemotron-page-elements-v3","displayName":"nemotron-page-elements-v3","publisher":"nvidia","shortDescription":"Model for object detection, fine-tuned to detect charts, tables, and titles in documents.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/nemotron-page-elements-v3.jpg","labels":"$39:0:props:children:0:props:children:1:props:children:props:children:props:children:3:props:children:0:props:children:props:labels","attributes":[{"key":"AVAILABLE","value":"true"},{"key":"PREVIEW","value":"false"},{"key":"last_month_api_invocation_count","value":"232060"},{"key":"last_month_download_count","value":"0"}],"updatedDate":"2026-03-04T21:39:31.777Z","bias":"| Field | Response |\n| ----- | ----- |\n| Participation considerations from adversely impacted groups [protected classes](https://calcivilrights.ca.gov/disputeresolution/protected-characteristics/) in model design and testing | None |\n| Measures taken to mitigate against unwanted bias | None |","canGuestDownload":true,"createdDate":"2026-03-04T21:39:31.777Z","description":"$41","explainability":"$42","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy","privacy":"| Field | Response |\n| ----- | ----- |\n| Generatable or reverse engineerable personal data? | No |\n| Personal data used to create this model? | No |\n| Was consent obtained for any personal data used? | Not Applicable |\n| How often is the dataset reviewed? | Before Release |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |\n| Applicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |","safetyAndSecurity":"| Field | Response |\n| ----- | ----- |\n| Model Application Field(s): | Object Detection for Retrieval, focused on Enterprise |\n| Describe the life critical impact (if present). | Not Applicable |\n| Use Case Restrictions: | **GOVERNING TERMS:** The trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |"},"endpointName":"nemotron-page-elements-v3","endpointSpec":{"openAPISpec":{"openapi":"3.1.0","info":{"title":"NeMo Retriever YOLOX Page Elements v3","description":"You Only Look Once (YOLO) anchor-free object detection of charts and tables in structured images.","version":"0.3.0"},"paths":{"/v1/infer":{"post":{"summary":"Post V1 Infer","description":"Detect charts and tables in the provided images.","operationId":"post_v1_infer_v1_infer_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/YoloxRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/YoloxResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"x-nvai-meta":{"name":"Inference model on image","returns":"Returns a object list.\n","path":"create","examples":[{"name":"Image from example","input":{"image":"https://assets.ngc.nvidia.com/products/api-catalog/yolox/yolox1.jpg"},"requestJson":"$43","responseJson":"$44"},{"name":"Image from example","input":{"image":"https://assets.ngc.nvidia.com/products/api-catalog/yolox/yolox2.jpg"},"requestJson":"$45","responseJson":"$46"}],"templates":[{"title":"Default","requestEjs":{"python":"import requests, base64\n\ninvoke_url = \"https://ai.api.nvidia.com/v1/cv/nvidia/nemotron-page-elements-v3\"\n\nwith open(\"yolox1.png\", \"rb\") as f:\n image_b64 = base64.b64encode(f.read()).decode()\n\nassert len(image_b64) < 180_000, \\\n \"To upload larger images, use the assets API (see docs)\"\n\nheaders = {\n \"Authorization\": \"Bearer $NVIDIA_API_KEY\",\n \"Accept\": \"application/json\"\n}\n\npayload = {\n \"input\": [\n {\n \"type\": \"image_url\",\n \"url\": f\"data:image/png;base64,{image_b64}\"\n }\n ]\n}\n\nresponse = requests.post(invoke_url, headers=headers, json=payload)\n\nprint(response.json())\n","node.js":"import axios from 'axios';\nimport { readFile } from 'node:fs/promises';\n\nconst invokeUrl = \"https://ai.api.nvidia.com/v1/cv/nvidia/nemotron-page-elements-v3\";\n\nconst headers = {\n \"Authorization\": \"Bearer $NVIDIA_API_KEY\",\n \"Accept\": \"application/json\"\n};\n\nreadFile(\"yolox1.png\")\n .then(data => {\n const imageB64 = Buffer.from(data).toString('base64');\n if (imageB64.length > 180_000) {\n throw new Error(\"To upload larger images, use the assets API (see docs)\");\n }\n\n const payload = {\n \"input\": [\n {\n \"type\": \"image_url\",\n \"url\": `data:image/png;base64,${imageB64}`\n }\n ]\n };\n\n return axios.post(invokeUrl, payload, { headers: headers, responseType: 'json' });\n })\n .then(response => {\n console.log(JSON.stringify(response.data));\n })\n .catch(error => {\n console.error(error);\n });\n","curl":"image_b64=$( base64 -i yolox1.png )\n\naccept_header='Accept: application/json'\n\n# Construct the JSON payload\necho '{\n \"input\": [\n {\n \"type\": \"image_url\",\n \"url\": \"data:image/png;base64,'\"${image_b64}\"'\"\n }\n ]\n}' > payload.json\n\ncurl https://ai.api.nvidia.com/v1/cv/nvidia/nemotron-page-elements-v3 \\\n -H \"Authorization: Bearer $NVIDIA_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -H \"$accept_header\" \\\n -d @payload.json\n"},"response":"$47"}]}}},"/v1/health/live":{"get":{"summary":"Health Live","description":"Check if the service is running.","operationId":"health_live_v1_health_live_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HealthLiveResponse"}}}}}}},"/v1/health/ready":{"get":{"summary":"Health Ready","description":"Check if the service is ready to recieve traffic.","operationId":"health_ready_v1_health_ready_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HealthReadyResponse"}}}}}}},"/v1/metrics":{"get":{"summary":"Metrics","description":"Handler for metrics endpoint.","operationId":"metrics_v1_metrics_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"string","title":"Response Metrics V1 Metrics Get"}}}}}}},"/v1/license":{"get":{"summary":"License","description":"Handler for license endpoint.","operationId":"license_v1_license_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/LicenseEndpointModel"}}}}}}},"/v1/metadata":{"get":{"summary":"Metadata","description":"Handler for metadata endpoint.","operationId":"metadata_v1_metadata_get","responses":{"200":{"description":"Successful 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name of the license for the NIM container."},"path":{"type":"string","title":"Path","description":"The filepath within the container containing the license content."},"sha":{"type":"string","title":"Sha","description":"A SHA1 hash of the license contents."},"size":{"type":"integer","title":"Size","description":"The number of characters in the license content."},"url":{"type":"string","title":"Url","description":"The url where this license is hosted externally."},"type":{"type":"string","enum":["file"],"const":"file","title":"Type","description":"The format of the license content."},"content":{"type":"string","title":"Content","description":"The license text."}},"type":"object","required":["name","path","sha","size","url","type","content"],"title":"LicenseEndpointModel","description":"A model representing the license response."},"ManifestEndpointModel":{"properties":{"manifest_file":{"type":"string","title":"Manifest File","description":"The content of the manifest file describing the required model artifacts."}},"type":"object","required":["manifest_file"],"title":"ManifestEndpointModel","description":"A model representing the manifest response."},"MetadataEndpointModel":{"properties":{"assetInfo":{"items":{"type":"string"},"type":"array","title":"Assetinfo","description":"A list of required container assets excluding model artifacts"},"licenseInfo":{"$ref":"#/components/schemas/LicenseEndpointModel","description":"The license info."},"modelInfo":{"items":{"$ref":"#/components/schemas/ModelInfo"},"type":"array","title":"Modelinfo","description":"A list of models being served by the NIM."},"version":{"type":"string","title":"Version","description":"The version of the NIM service."}},"type":"object","required":["assetInfo","licenseInfo","modelInfo","version"],"title":"MetadataEndpointModel","description":"A model representing the metadata response."},"ModelInfo":{"properties":{"modelUrl":{"type":"string","title":"Modelurl"},"shortName":{"type":"string","title":"Shortname"}},"type":"object","required":["modelUrl","shortName"],"title":"ModelInfo","description":"A model representing the model response."},"Type":{"type":"string","enum":["chart","table","title"],"title":"Type"},"ValidationError":{"properties":{"loc":{"items":{"anyOf":[{"type":"string"},{"type":"integer"}]},"type":"array","title":"Location"},"msg":{"type":"string","title":"Message"},"type":{"type":"string","title":"Error 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processed."}},"type":"object","required":["url"],"title":"YoloxImageUrl"},"YoloxMessage":{"properties":{"role":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Role","default":""},"content":{"items":{"$ref":"#/components/schemas/YoloxImage"},"type":"array","minItems":1,"title":"Content"}},"type":"object","required":["content"],"title":"YoloxMessage"},"YoloxRequest":{"properties":{"model":{"type":"string","enum":["nvidia/nemotron-page-elements-v3"],"const":"nvidia/nemotron-page-elements-v3","title":"Model","default":"nvidia/nemotron-page-elements-v3"},"messages":{"items":{"$ref":"#/components/schemas/YoloxMessage"},"type":"array","maxItems":1,"minItems":1,"title":"Messages"},"confidence_threshold":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Confidence Threshold","description":"Threshold for filtering low-confidence predictions","default":0.01},"nms_threshold":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Nms Threshold","description":"Threshold for filtering overlapping predictions via non-max suppression","default":0.5}},"type":"object","required":["messages"],"title":"YoloxRequest"},"YoloxResponse":{"properties":{"object":{"type":"string","enum":["list"],"const":"list","title":"Object","default":"list"},"data":{"items":{"items":{"$ref":"#/components/schemas/YoloxResponseItem"},"type":"array"},"type":"array","title":"Data"},"model":{"type":"string","title":"Model"},"usage":{"anyOf":[{"type":"object"},{"type":"null"}],"title":"Usage"}},"type":"object","required":["data","model"],"title":"YoloxResponse"},"YoloxResponseItem":{"properties":{"bboxes":{"items":{"$ref":"#/components/schemas/YoloxBoundingBox"},"type":"array","title":"Bboxes"},"type":{"$ref":"#/components/schemas/Type"}},"type":"object","required":["bboxes","type"],"title":"YoloxResponseItem"}}}},"namespace":"qc69jvmznzxy","updatedDate":"2026-08-10T20:29:54.288Z","nvcfFunctionId":"2a11df34-a454-4d98-b072-4f8dd7b3c6a2","createdDate":"2026-03-04T21:39:32.251Z","attributes":{"apiDocsUrl":"https://docs.nvidia.com/nim/ingestion/object-detection/latest/api-reference.html","termsOfUse":"GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Open Model License Agreement.\n","showUnavailableBanner":false,"cta":"$39:0:props:children:0:props:children:1:props:children:props:children:props:children:3:props:children:1:props:children:1:props:cta","deploy":[{"label":"Linux with Docker","filename":"linux.md","contents":"$48"}]},"artifactName":"nemotron-page-elements-v3"},"isLoginRequired":true,"playgroundElement":"$49","publisherName":"nvidia","searchParams":{"model":"nemotron-page-elements-v3"},"tabs":[{"label":"Experience","bare":true,"icon":"play","kind":"default","id":"experience","href":"/nvidia/nemotron-page-elements-v3","aliases":["/nvidia/nemotron-page-elements-v3/experience"]},{"label":"Model Card","icon":"book","kind":"default","id":"modelcard","href":"/nvidia/nemotron-page-elements-v3/modelcard","aliases":"$undefined"},{"label":"Deploy","kind":"default","id":"deploy","href":"/nvidia/nemotron-page-elements-v3/deploy","aliases":"$undefined"}]}]}]}]]}]] 3a:["$","$L4a",null,{"spec":"$39:0:props:children:1:props:children:props:children:props:endpointSpec"}]