Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
The NeMo Retriever Graphic Elements v1 model is a specialized object detection system designed to identify and extract key elements from charts and graphs. Based on YOLOX, an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. While the underlying technology builds upon work from Megvii Technology, we developed our own base model through complete retraining rather than using pre-trained weights.
The model excels at detecting and localizing various graphic elements within chart images, including titles, axis labels, legends, and data point annotations. This capability makes it particularly valuable for document understanding tasks and automated data extraction from visual content.
This model is ready for commercial use 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 supersedes the CACHED model.
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Deployment Geography: Global
Use Case:
This model is designed for automating extraction of graphic elements of charts in enterprise documents. Key applications include:
Release Date: 2025-03-17
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. The model is fine-tuned to detect 10 classes of objects in documents:
Input type(s): Image
Input format(s): Red, Green, Blue (RGB)
Input parameters: Two Dimensional (2D)
Other properties related to input: Expected input is a np.ndarray
image of shape [Channel, Width, Height]
, or an np.ndarray
batch of image of shape [Batch, Channel, Width, Height]
.
Output type(s): Text associated to each of the following classes :
["chart_title", "x_title", "y_title", "xlabel", "ylabel", "other", "legend_label", "legend_title", "mark_label", "value_label"]
Output format: Dict of String
Output parameters: 1D
Other properties related to output: None
Runtime Engine: NeMo Retriever Graphic Elements v1 NIM
Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace
Supported Operating System(s): Linux
nemoretriever-graphic-elements-v1
PubMed Central (PMC) Chart Dataset
DeepRule dataset
Results were evaluated using the PMC Chart dataset. The Mean Average Precision (mAP) was used as the evaluation metric to measure the model's ability to correctly identify and localize objects across different confidence thresholds.
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 | 11,520 |
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 |
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 |
Engine: Tensor(RT)
Test hardware: Tested on all supported hardware listed in compatibility section
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