TexasInstruments-EdgeAI/RTMDet-Detection

RTMDet for TI EdgeAI

Real-Time Object Detector with a CSPNeXt Backbone

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Overview

RTMDet is a high-performance real-time object detector from OpenMMLab with a CSPNeXt backbone and an efficient anchor-free detection head. It achieves excellent accuracy-speed trade-offs across five model sizes (tiny, s, m, l, x), making it suitable for a wide range of deployment scenarios from resource-constrained edge devices to high-throughput server deployments.

This RTMDet model is optimized for Texas Instruments MPU (Microprocessor Unit) devices, enabling high-performance computer vision applications at the edge. Whether you're building industrial automation systems, smart cameras, robotics, or IoT vision solutions, this model provides production-ready object detection with minimal setup.

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Model Variants

Model	Input Size	mAP[.5:.95]%	Validated Devices	Config
rtmdet_tiny	640x640	40.9	TDA4VH	rtmdet_tiny_config.yaml
rtmdet_s	640x640	44.5	TDA4VH	rtmdet_s_config.yaml
rtmdet_m	640x640	49.3	TDA4VH	rtmdet_m_config.yaml
rtmdet_l	640x640	51.4	TDA4VH	rtmdet_l_config.yaml
rtmdet_x	640x640	52.8	TDA4VH	rtmdet_x_config.yaml

Recommended for edge deployment: rtmdet_tiny (smallest, best accuracy/compute trade-off)

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Quick Start

Prerequisites

pip install onnx>=1.22.0
pip install onnxruntime>=1.23.2
pip install onnxsim  # For model simplification

Export the Model

# Export all variants (default)
python prepare_model.py

# Export specific variants
python prepare_model.py --models tiny
python prepare_model.py --models tiny s m

# Export without ONNX simplification
python prepare_model.py --models tiny --no-simplify

# Force regeneration of .link files
python prepare_model.py --generate-links

The script automatically:

• Installs mmcv-lite and mmdet (and other required dependencies)
• Downloads the PyTorch checkpoint referenced by each variant's .onnx.link file from OpenMMLab
• Downloads the matching mmdetection config files (pinned to tag v3.3.0)
• Builds the model with mmdet.apis.init_detector and wraps it to emit decoded boxes (xyxy) and per-class sigmoid scores (NMS is left for on-device post-processing)
• Exports to ONNX (opset 13), fixes the batch dimension to 1, and re-runs shape inference
• Optionally simplifies the model using onnx-simplifier

Compile and Infer uing edgeai-tidlrunner

Note: Run the commands below from inside the tidlrunner directory (the cloned edgeai-tidlrunner repository), with --config_path pointing to this model's config file.

Compile using edgeai-tidlrunner - on PC

cd /path/to/edgeai-tidlrunner
tidlrunner-cli compile --target_device J784S4 \
  --config_path /path/to/rtmdet_tiny_config.yaml

Run Inference Benchmark - on device

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device J784S4 \
  --config_path /path/to/rtmdet_tiny_config.yaml

Compile and Infer using edgeai-tidl-tools (Advanced):

Follow the instructions at https://github.com/TexasInstruments/edgeai-tidl-tools

Deploy using edgeai-tidl-tools:

Deplyment can be done using edgeai-tidl-tools. For ONNX models, onnxruntime-tidl with TIDL acceleration can be used. Consult the documentation of edgeai-tidl-tools for more details.

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Citation

If you use RTMDet in your research, please cite:

@article{lyu2022rtmdet,
  title={RTMDet: An Empirical Study of Designing Real-Time Object Detectors},
  author={Lyu, Chengqi and Zhang, Wenwei and Huang, Haian and Zhou, Yue and Wang, Yudong and Liu, Yanyi and Zhang, Shilong and Chen, Kai},
  journal={arXiv preprint arXiv:2212.07784},
  year={2022}
}

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🔗 Resources

Resource	Link
Paper	arXiv:2212.07784
Source Code	open-mmlab/mmdetection (rtmdet configs)
edgeai-tidl-tools	GitHub
edgeai-tidlrunner	GitHub
EdgeAI SDK	Documentation
EdgeAI Ecosystem	GitHub

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Related Models

YOLOX Anchor-free CNN detector Similar single-stage design

YOLOv8 CNN-based real-time detector Comparable accuracy/speed range

YOLO11 Latest Ultralytics YOLO Improved efficiency

RT-DETRv2 Real-time transformer detector NMS-free alternative

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Maintained by: Texas Instruments EdgeAI Team
Last Updated: August 2026