Yolov8n_seg
Use case : Instance segmentation
Model description
Yolov8n_seg is a lightweight and efficient model designed for instance segmentation tasks. It is part of the YOLO (You Only Look Once) family of models, known for their real-time object detection capabilities. The "n" in Yolov8n_seg indicates that it is a nano version, optimized for speed and resource efficiency, making it suitable for deployment on devices with limited computational power, such as mobile devices and embedded systems.
Yolov8n_seg is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter.
Network information
| Network Information | Value |
|---|---|
| Framework | Tensorflow |
| Quantization | int8 |
| Paper | https://arxiv.org/pdf/2305.09972 |
Recommended platform
| Platform | Supported | Recommended |
|---|---|---|
| STM32L0 | [] | [] |
| STM32L4 | [] | [] |
| STM32U5 | [] | [] |
| STM32MP1 | [] | [] |
| STM32MP2 | [x] | [] |
| STM32N6 | [x] | [x] |
Performances
Metrics
Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
Reference NPU memory footprint based on COCO dataset
| Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|---|
| Yolov8n seg per channel | COCO | Int8 | 256x256x3 | STM32N6 | 2128 | 0.0 | 3425.39 | 10.0.0 | 2.0.0 |
| Yolov8n seg per channel | COCO | Int8 | 320x320x3 | STM32N6 | 2564.06 | 0.0 | 3467.56 | 10.0.0 | 2.0.0 |
Reference NPU inference time based on COCO Person dataset
| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|---|
| YOLOv8n seg per channel | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 37.59 | 26.61 | 10.0.0 | 2.0.0 |
| YOLOv8n seg per channel | COCO-Person | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 53.21 | 18.79 | 10.0.0 | 2.0.0 |
Retraining and Integration in a Simple Example
Please refer to the stm32ai-modelzoo-services GitHub here. For instance segmentation, the models are stored in the Ultralytics repository. You can find them at the following link: Ultralytics YOLOv8-STEdgeAI Models.
Please refer to the Ultralytics documentation to retrain the model.
References
[1] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, "Microsoft COCO: Common Objects in Context." European Conference on Computer Vision (ECCV), 2014. Link
[2] Ultralytics, "YOLOv8: Next-Generation Object Detection and Segmentation Model." Ultralytics, 2023. Link