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56:["$","$L59",null,{"hasPartnerEndpoint":false}] 5d:T20e2,## Model Overview ### Description The **NeMo Retriever Page Elements v3** model is a specialized object detection model designed to identify and extract key elements from charts and graphs. While the underlying technology builds upon work from [Megvii Technology](https://github.com/Megvii-BaseDetection/YOLOX), we developed our own base model through complete retraining rather than using pre-trained weights. YOLOX is an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. The model is trained to detect **tables**, **charts**, **infographics**, **titles**, **header/footers** and **texts** in documents. This model supersedes the [nemoretriever-page-elements](https://build.nvidia.com/nvidia/nemoretriever-page-elements-v2) model and is a part of the NVIDIA NeMo Retriever family of NIM microservices specifically for object detection and multimodal extraction of enterprise documents. This model is ready for commercial/non-commercial use. ### License/Terms of use The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). **You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.** ### Deployment Geography Global ### Use Case The **NeMo Retriever Page Elements v3** model is designed for automating extraction of text, charts, tables, infographics etc in enterprise documents. It can be used for document analysis, understanding and processing. Key applications include: - Enterprise document extraction, embedding and indexing - Augmenting Retrieval Augmented Generation (RAG) workflows with multimodal retrieval - Data extraction from legacy documents and reports ### Release Date 11/15/2025 via https://build.nvidia.com/nvidia/nemoretriever-page-elements-v3 ### References - YOLOX paper: https://arxiv.org/abs/2107.08430 - YOLOX repo: https://github.com/Megvii-BaseDetection/YOLOX - Previous version of the Page Element model: https://build.nvidia.com/nvidia/nemoretriever-page-elements-v2 - Technical blog: 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 the classification and one for the bounding box prediction). YOLOX is a single-stage object detector that improves on Yolo-v3.
**This model was developed based on the Yolo architecture**
**Number of model parameters**: 5.4 × 10⁷
### Input **Input Type(s)**: Image
**Input Format(s)**: Red, Green, Blue (RGB)
**Input Parameters**: Two-Dimensional (2D)
**Other Properties Related to Input**: Image size resized to `(1024, 1024)` ### Output **Output Type(s)**: Array
**Output Format**: A dictionary of dictionaries containing `np.ndarray` objects. The outer dictionary has entries for each sample (page), and the inner dictionary contains a list of dictionaries, each with a bounding box (`np.ndarray`), class label, and confidence score for that page.
**Output Parameters**: One-Dimensional (1D)
**Other Properties Related to Output**: The output contains bounding boxes, detection confidence scores, and object classes (chart, table, infographic, title, text, headers and footers). The thresholds used for non-maximum suppression are `conf_thresh=0.01` and `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 that is more complex than a chart, including diagrams and flowcharts * Maps are _not_ considered infographics * Title * Titles can be section titles, or table/chart/infographic titles * Header/footer * Page headers and footers * Text * Texts are 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 Engine(s):** - **NeMo Retriever Page Elements v3** NIM **Supported Hardware Microarchitecture Compatibility [List in Alphabetic Order]:** - NVIDIA Ampere - NVIDIA Hopper - NVIDIA Lovelace **Preferred/Supported Operating System(s):** - 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. This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above. ## Model Version(s): * `nemoretriever-page-elements-v3` ## Training and Evaluation Datasets: ### Training Dataset **Data Modality**: Image
**Image Training Data Size**: Less than a Million Images
**Data collection method by dataset**: Automated
**Labeling method by dataset**: Hybrid: Automated, Human
**Pretraining (by NVIDIA)**: 118,287 images of the [COCO train2017](https://cocodataset.org/#download) dataset
**Finetuning (by NVIDIA)**: 36,093 images from [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 data annotation team
**Number of bounding boxes per class**: 35,328 tables, 44,178 titles, 11,313 charts and 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 The primary evaluation set is a cut of the Azure labels and digital corpora images. Number of 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, which measures the model's ability to correctly identify and localize objects across different confidence thresholds. **Data collection method by dataset**: Hybrid: Automated, Human
**Labeling method by dataset**: Hybrid: Automated, Human
**Properties**: 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: **Acceleartion Engine**: TensorRT
**Test hardware**: See [Support Matrix from NIM documentation](https://docs.nvidia.com/nim/ingestion/object-detection/latest/support-matrix.html#) ## 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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. **For more detailed information on ethical considerations for this model**, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ## Get Help ### Enterprise Support Get access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).5e:T75a,| 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: | Use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/) and the [Apache 2.0 License](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE). |57:[["$","div",".3",{"className":"relative","children":[["$","div",null,{"className":"relative flex min-h-[194px] w-full flex-col overflow-hidden p-md sm:p-ml bg-manitoulinLightBlack","children":[["$","$L5a",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/nemoretriever-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":["$","div",null,{"className":"flex flex-col gap-1.5 whitespace-pre-wrap","children":[["$","$L5b",null,{"className":"w-fit text-ml text-n300 lowercase hover:text-n050","href":"/nvidia","children":"NVIDIA"}],["$","div",null,{"className":"flex flex-col items-start gap-3 lg:flex-row lg:items-center","children":[["$","h1",null,{"className":"max-w-full text-lg leading-heading font-medium break-words text-primary","data-testid":"resource-name","id":"resource-name","children":"nemoretriever-page-elements-v3"}],["$","$L5c",null,{"artifact":{"artifactType":"ENDPOINT","name":"nemoretriever-page-elements-v3","displayName":"nemoretriever-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/nemoretriever-page-elements-v3.jpg","labels":["Chart Detection","Object Detection","Table Detection","data ingestion","nemo retriever","nimType:endpoint:nim_type_run_anywhere","usecase:endpoint:usecase_object_detect"],"attributes":[{"key":"AVAILABLE","value":"true"},{"key":"DEPRECATION","value":"05/18/2026"},{"key":"PREVIEW","value":"false"}],"updatedDate":"2025-12-19T03:46:33.010Z","bias":"| Field | Response |\n| ----- | ----- |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |\n| Measures taken to mitigate against unwanted bias | None |","canGuestDownload":true,"createdDate":"2025-12-19T03:46:33.010Z","description":"$5d","explainability":"$5e","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: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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} ] } 67: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 } ] } } ] } 68: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 } ] } } ] } 69:T591,## 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=$NGC_API_KEY \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \ -u $(id -u) \ -p 8000:8000 \ nvcr.io/nim/nvidia/nemoretriever-page-elements-v3:latest ``` ## Step 3. Test the NIM You can now make a local API call using this curl command: ```bash HOSTNAME="localhost" SERVICE_PORT=8000 curl -X "POST" \ "http://${HOSTNAME}:${SERVICE_PORT}/v1/infer" \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "input": [ { "type": "image_url", "url": "data:image/png;base64," }, { "type": "image_url", "url": "data:image/png;base64," } ] }' ``` For more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/ingestion/object-detection/latest/getting-started.html#get-started-with-nvidia-nim-for-object-detection).58:["$","$L63",null,{"spec":{"openAPISpec":{"openapi":"3.1.0","info":{"title":"NeMo Retriever YOLOX Page Elements v3","description":"You Only Look Once (YOLO) anchor-free object detection of charts and tables in structured images.","version":"0.3.0"},"paths":{"/v1/infer":{"post":{"summary":"Post V1 Infer","description":"Detect charts and tables in the provided images.","operationId":"post_v1_infer_v1_infer_post","requestBody":{"content":{"application/json":{"schema":{"$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":"$64","responseJson":"$65"},{"name":"Image from example","input":{"image":"https://assets.ngc.nvidia.com/products/api-catalog/yolox/yolox2.jpg"},"requestJson":"$66","responseJson":"$67"}],"templates":[{"title":"Default","requestEjs":{"python":"import requests, base64\n\ninvoke_url = \"https://ai.api.nvidia.com/v1/cv/nvidia/nemoretriever-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/nemoretriever-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/nemoretriever-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":"$68"}]}}},"/v1/health/live":{"get":{"summary":"Health Live","description":"Check if the service is running.","operationId":"health_live_v1_health_live_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$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 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