--- license: agpl-3.0 tags: - vision - image-detection datasets: - COCO ---
# YOLOv8 for TI EdgeAI ### Real-Time Anchor-Free Object Detector [![License](https://img.shields.io/badge/License-AGPL%203.0-blue?style=for-the-badge)](https://opensource.org/licenses/AGPL-3.0) [![Framework](https://img.shields.io/badge/Framework-ONNX-orange?style=for-the-badge)](https://onnx.ai/) [![Task](https://img.shields.io/badge/Task-Object%20Detection-green?style=for-the-badge)](https://github.com/TexasInstruments/edgeai) [![Dataset](https://img.shields.io/badge/Dataset-COCO-blueviolet?style=for-the-badge)](https://cocodataset.org/)
--- ## 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. --- ## 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](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](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) --- ## Quick Start ### Prerequisites ```bash pip install onnx>=1.22.0 pip install onnxruntime>=1.23.2 pip install ultralytics ``` For TI hardware deployment, also set up **[tidlrunner](https://github.com/TexasInstruments/edgeai-tidlrunner/blob/main/README.md)**. If accessing this model from HuggingFace, clone the repository using the `hf` CLI: ```bash hf download --local-dir ``` ### Export the Model ```bash # 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](https://github.com/TexasInstruments/edgeai-tidlrunner) repository), with `--config_path` pointing to this model's config file. **Compile using edgeai-tidlrunner - on PC** ```bash cd /path/to/edgeai-tidlrunner tidlrunner-cli compile --target_device J784S4 \ --config_path /path/to/yolov8n_config.yaml ``` **Run Inference Benchmark - on device** ```bash 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](https://github.com/TexasInstruments/edgeai-tidl-tools)**. For ONNX models, onnxruntime-tidl with TIDL acceleration can be used. Consult the documentation of edgeai-tidl-tools for more details. --- ## 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: ```bibtex @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} } ``` --- ## 🔗 Resources | Resource | Link | |----------|------| | **Source Code** | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) | | **Documentation** | [YOLOv8 Docs](https://docs.ultralytics.com/models/yolov8/) | | **edgeai-tidl-tools** | [GitHub](https://github.com/TexasInstruments/edgeai-tidl-tools) | | **edgeai-tidlrunner** | [GitHub](https://github.com/TexasInstruments/edgeai-tidlrunner) | | **EdgeAI SDK** | [Documentation](https://github.com/TexasInstruments/edgeai/blob/main/edgeai-mpu/readme_sdk.md) | | **EdgeAI MPU Overview** | [GitHub](https://github.com/TexasInstruments/edgeai/tree/main/edgeai-mpu) | | **TI EdgeAI Ecosystem** | [GitHub](https://github.com/TexasInstruments/edgeai) | --- ## 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