TexasInstruments-EdgeAI/YOLOX-Detection

YOLOX for TI EdgeAI

Anchor-Free YOLO with a Decoupled Head

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Overview

YOLOX is an anchor-free member of the YOLO family developed by Megvii, designed to close the gap between research and industrial object detection. Unlike earlier YOLO versions, YOLOX removes predefined anchor boxes and instead predicts objects directly, which simplifies the design and reduces the number of heuristic tuning parameters (e.g., anchor sizes) needed for a new dataset.

Two architectural changes distinguish YOLOX from anchor-based YOLO detectors: a decoupled head that separates classification and localization into independent branches (improving convergence and accuracy over the coupled head used in YOLOv3/v4/v5), and SimOTA, an advanced label-assignment strategy that formulates matching between predictions and ground-truth boxes as an optimal-transport problem to pick better positive samples during training.

This model is optimized for deployment on Texas Instruments edge devices, providing production-ready object detection for industrial automation, smart cameras, robotics, and IoT vision applications. It offers a range of variants — from the lightweight yolox-nano to the high-accuracy yolox-x — covering a wide accuracy/compute trade-off space.

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Model Variants

Model	Model ID	Input Size	mAP[.5:.95]%	Validated Devices	Config
yolox_nano	od-mh8009	416×416	24.8	TDA4VH, TDA4VL	yolox_nano_config.yaml
yolox_tiny	od-mh8010	416×416	32.8	TDA4VH, TDA4VL	yolox_tiny_config.yaml
yolox_s	-	640×640	-	-	N/A
yolox_m	od-mh8011	640×640	46.9	TDA4VH, TDA4VL	yolox_m_config.yaml
yolox_l	od-mh8012	640×640	49.7	TDA4VH, TDA4VL	yolox_l_config.yaml
yolox_x	od-mh8013	640×640	51.2	TDA4VH, TDA4VL	yolox_x_config.yaml
yolox_darknet53	od-mh8014	640×640	47.4	TDA4VH, TDA4VL	yolox_darknet53_config.yaml

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

yolox_s currently ships only as an ONNX export (yolox_s.onnx) with no TIDL config YAML available in this folder.

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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

# Prepare a specific variant (downloads pre-built ONNX, or falls back to
# downloading the .pth checkpoint and converting it locally)
python prepare_model.py --model yolox_nano

# Prepare multiple variants at once
python prepare_model.py --model yolox_nano yolox_tiny yolox_m

# Prepare every supported variant
python prepare_model.py --model all

# Force re-download even if the ONNX/PTH file already exists locally
python prepare_model.py --model yolox_nano --force-download

# Prepare and verify accuracy on COCO val2017
python prepare_model.py --model yolox_nano --verify --num-val-images 500

# List all supported variants and their local download status
python prepare_model.py --list-models

The script automatically:

• Reads the source URL from each variant's .onnx.link file and downloads the pre-built ONNX from GitHub releases
• Falls back to downloading the PyTorch checkpoint from the .pth.link file and converting it to ONNX locally (via the official YOLOX yolox.exp.get_exp API) if the pre-built ONNX is unavailable
• Runs ONNX shape inference and hard-codes the batch dimension to 1
• Optionally simplifies the model using onnx-simplifier
• Optionally verifies accuracy on COCO val2017 using pycocotools, reporting mAP@[0.50:0.95] and mAP@0.50

Supported --model values: yolox_nano, yolox_tiny, yolox_s, yolox_m, yolox_l, yolox_x, yolox_darknet53, or all.

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/yolox_nano_config.yaml

Run Inference Benchmark - on device

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device J784S4 \
  --config_path /path/to/yolox_nano_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 YOLOX in your research, please cite:

@article{yolox2021,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}

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🔗 Resources

Resource	Link
Paper	arXiv:2107.08430
Source Code	Megvii-BaseDetection/YOLOX
edgeai-tidl-tools	GitHub
edgeai-tidlrunner	GitHub
EdgeAI SDK	Documentation
EdgeAI Ecosystem	GitHub

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