TexasInstruments/YOLOv8-Detection

YOLOv8 for TI EdgeAI

Real-Time Anchor-Free Object Detector

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Overview

YOLOv8 is Ultralytics' real-time object detector, building on the advancements of previous YOLO versions with an anchor-free split Ultralytics head design that improves accuracy and speeds up the detection process compared to earlier anchor-based approaches. It combines a state-of-the-art backbone and neck architecture for improved feature extraction with an optimized accuracy-speed trade-off, making it suitable for real-time detection across a wide range of applications.

YOLOv8 is offered in five size variants — n, s, m, l, x — spanning a wide accuracy-speed trade-off, from resource-constrained edge devices up to high-throughput deployments. This model is optimized for Texas Instruments MPU (Microprocessor Unit) devices, targeting edge computer vision use cases such as industrial automation, smart cameras, robotics, and IoT vision.

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Model Variants

Model	Params (M)	Input Size	Reference mAP[.5:.95]%	Validated Devices	Config
yolov8n	3.2	640×640	37.3	TDA4VH, TDA4VL, TDA4AEN	yolov8n_config.yaml
yolov8s	11.2	640×640	44.9	N/A	N/A
yolov8m	25.9	640×640	50.2	TDA4VH, TDA4VL	yolov8m_config.yaml
yolov8l	43.7	640×640	52.9	N/A	N/A
yolov8x	68.2	640×640	53.9	N/A	N/A

Recommended for edge deployment: yolov8n (best accuracy/compute trade-off, smallest footprint)

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Quick Start

Prerequisites

pip install onnx>=1.22.0
pip install onnxruntime>=1.23.2
pip install ultralytics

For TI hardware deployment, also set up tidlrunner.

If accessing this model from HuggingFace, clone the repository using the hf CLI:

hf download <REPO_ID> --local-dir <download_location>

Export the Model

# Export the default model (yolov8n)
python prepare_model.py

# Export specific variants
python prepare_model.py --models yolov8n yolov8m

# Export all variants
python prepare_model.py --models all

# List all supported variants
python prepare_model.py --list-models

# Export with a custom output directory or export format
python prepare_model.py --models yolov8n --output-dir ./exports --format onnx

The script automatically:

• Installs required runtime dependencies (onnx, ultralytics, onnxslim, onnxruntime)
• Loads the requested Ultralytics YOLOv8 checkpoint (.pt), downloading it on first use
• Exports the model to ONNX (opset 19 by default, configurable via --opset)
• Supports alternate export formats (torchscript, tflite, pb, saved_model, coreml, and more) via --format

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/yolov8n_config.yaml

Run Inference Benchmark - on device

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device J784S4 \
  --config_path /path/to/yolov8n_config.yaml

To evaluate accuracy instead, replace infer with evaluate in the command above.

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

Ultralytics has not published a formal research paper for YOLOv8 due to the rapidly evolving nature of the models. If you use the YOLOv8 model or any other software from the Ultralytics repository in your work, please cite it using the following format:

@software{yolov8_ultralytics,
  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
  title = {Ultralytics YOLOv8},
  version = {8.0.0},
  year = {2023},
  url = {https://github.com/ultralytics/ultralytics},
  orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
  license = {AGPL-3.0}
}

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🔗 Resources

Resource	Link
Source Code	ultralytics/ultralytics
Documentation	YOLOv8 Docs
edgeai-tidl-tools	GitHub
edgeai-tidlrunner	GitHub
EdgeAI SDK	Documentation
EdgeAI MPU Overview	GitHub
TI EdgeAI Ecosystem	GitHub

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Related Models

YOLO11 Newer Ultralytics generation Improved accuracy/speed

YOLO26 Latest Ultralytics generation Unified, end-to-end detection

YOLOX Anchor-free YOLO variant Decoupled head design

RTMDet CNN-based alternative Real-time mmdetection model

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