nemotron-graphic-elements-v1

Description

nemotron-graphic-elements-v1 is a specialized object detection model designed to identify and extract key elements from charts and graphs. Based on YOLOX (an anchor-free version of YOLO), it detects and localizes graphic elements including titles, axis labels, legends, and data point annotations. While the underlying approach builds upon work from the YOLOX ecosystem, NVIDIA developed its own base model through complete retraining rather than using pre-trained weights.

This model supersedes the CACHED model.

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. Use of this model is governed by the NVIDIA Open Model License Agreement.

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Model Developer: NVIDIA

Deployment Geography:

Global

Use Case:

This model is designed for automating extraction of graphic 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

Release Date:

Build.NVIDIA.com 03/02/2026 via nemotron-graphic-elements-v1

Reference(s):

References:

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)
Classes Detected: chart_title, x_title, y_title, xlabel, ylabel, other, legend_label, legend_title, mark_label, value_label
Number of Model Parameters: 5.4e7

Input:

Input Types: Image
Input Formats: RGB
Input Parameters: Two Dimensional (2D)
Other Input Properties: Expects an image array (single image or batch). Expected input is a np.ndarray image of shape [Channel, Width, Height], or an np.ndarray batch of images of shape [Batch, Channel, Width, Height].

Output:

Output Types: Structured detections (bounding boxes + labels + confidence)
Output Format: Dict / JSON-compatible structure
Output Parameters: One Dimensional (1D)
Other Output Properties: Outputs detections per input image.

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

Inference

Acceleration Engine: TensorRT
Test Hardware: Tested on supported hardware in the compatibility section

Model Version(s)

nemotron-graphic-elements-v1

Short Name: nemotron-graphic-elements-v1

Training and Evaluation Datasets:

Training Dataset

Data Modality: Image
Training Data Collection: Hybrid (Automated + Human)
Training Labeling: Hybrid (Automated + Human)
Training Properties: Trained using a mixture of real-world chart images and pseudo-labeled charts, including:

PubMed Central (PMC) Chart Dataset (link): A real-world dataset collected from PubMed Central documents and manually annotated (ICPR 2022 CHART-Infographic competition). Includes 5,614 images for chart element detection, 4,293 images for plot detection/data extraction, and 22,924 images for chart classification.
DeepRule dataset (link): A large dataset of chart images crawled from public Excel sheets with text overwritten to protect privacy. Relevant classes were pseudo-labeled using the CACHED model; training used a subsample of 9,091 charts where a title was detected, alongside the PMC training images.

Evaluation Dataset

Evaluation Data Collection: Hybrid (Automated + Human)
Evaluation Labeling: Hybrid (Automated + Human)
Evaluation Properties: Evaluated using the PMC Chart dataset. Mean Average Precision (mAP) was used as the evaluation metric. The validation dataset is the same as the PMC Chart dataset.

Number of bounding boxes and images per class:

Label	Images	Boxes
chart_title	38	38
legend_label	318	1077
legend_title	17	19
mark_label	42	219
other	113	464
value_label	52	726
x_title	404	437
xlabel	553	4091
y_title	502	505
ylabel	534	3944
Total	560	11520

Per-class Performance Metrics

Average Precision (AP)

Class	AP	Class	AP	Class	AP
chart_title	82.38	x_title	88.77	y_title	89.48
xlabel	85.04	ylabel	86.22	other	55.14
legend_label	84.09	legend_title	60.61	mark_label	49.31
value_label	62.66

Average Recall (AR)

Class	AR	Class	AR	Class	AR
chart_title	93.16	x_title	92.31	y_title	92.32
xlabel	88.93	ylabel	89.40	other	79.48
legend_label	88.07	legend_title	68.42	mark_label	73.61
value_label	68.32

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.

Ethical Considerations

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