TexasInstruments-EdgeAI/YOLO26-Detection

YOLO26 for TI EdgeAI

Native End-to-End Object Detector for Real-Time Edge Deployment

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Overview

YOLO26 is the newest generation of the Ultralytics YOLO family, released in January 2026. Its detection head is natively end-to-end: by default it predicts final boxes directly, without a separate non-maximum suppression (NMS) post-processing step, which simplifies deployment and reduces post-processing latency. The head also removes Distribution Focal Loss (DFL) from box regression, lowering head complexity while keeping an unconstrained regression range.

The training recipe pairs these architectural changes with MuSGD (a hybrid Muon + SGD optimizer), Progressive Loss (which shifts supervision emphasis toward the inference-time head), and STAL, a Small-Target-Aware Label Assignment scheme that preserves positive label coverage for small objects. Together these updates improve the accuracy/latency trade-off over YOLO11 across all five model scales and give YOLO26n notably faster CPU ONNX inference, making the family well suited to power- and latency-constrained edge deployments.

These ONNX models cover the five COCO-pretrained detection scales (n/s/m/l/x, 80 classes), exported and shape-fixed to a static 640×640 input for TIDL compilation on TI edge SoCs.

See YOLO11 for the previous-generation, NMS-based YOLO models.

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Model Variants

Model	Input Size	mAP[.5:.95]%	Validated Devices	Config
yolo26n	640×640	40.9	TDA4VH, TDA4VL	yolo26n_model_config.yaml
yolo26s	640×640	48.6	TDA4VH, TDA4VL	yolo26s_model_config.yaml
yolo26m	640×640	53.1	TDA4VH, TDA4VL	yolo26m_model_config.yaml
yolo26l	640×640	55.0	TDA4VH, TDA4VL	yolo26l_model_config.yaml
yolo26x	640×640	57.5	TDA4VH, TDA4VL	yolo26x_model_config.yaml

Recommended for edge deployment: yolo26n (best accuracy/compute trade-off)

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Quick Start

Prerequisites

pip install onnx>=1.22.0 onnxruntime>=1.23.2

Export the Model

# Prepare the default model (yolo26n)
python prepare_model.py

# Prepare a specific model variant
python prepare_model.py --model yolo26s

# Prepare multiple variants in one run
python prepare_model.py --model yolo26n yolo26s yolo26m

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

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

# Re-run shape fixing on an already-downloaded ONNX
python prepare_model.py --model yolo26n --skip-download

The script automatically:

• Parses the variant's .link file to get the HuggingFace download URL
• Downloads the model with curl if it isn't already present locally
• Fixes dynamic input dimensions to a static shape (default [1, 3, 640, 640])
• Runs ONNX shape inference and optional onnx-simplifier optimization
• Validates the resulting ONNX model structure

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/yolo26n_model_config.yaml

Run Inference Benchmark - on device

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device J784S4 \
  --config_path /path/to/yolo26n_model_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 these models, please cite:

@article{jocher2026yolo26,
  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and
          Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
  journal={arXiv preprint arXiv:2606.03748},
  year={2026}
}

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🔗 Resources

Resource	Link
Paper	arXiv:2606.03748
Source Code	ultralytics/ultralytics
Model Docs	YOLO26 Documentation
edgeai-tidl-tools	GitHub
edgeai-tidlrunner	GitHub
EdgeAI SDK	Documentation
EdgeAI Ecosystem	GitHub

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Related Models

YOLO11 Predecessor generation NMS-based detection

YOLOv8 Earlier YOLO generation Widely adopted baseline

YOLOX Anchor-free detector Decoupled head design

RT-DETRv2 Transformer-based detector Real-time DETR variant

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