## 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/).