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--- |
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license: other |
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license_name: sla0044 |
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license_link: >- |
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https://github.com/STMicroelectronics/stm32ai-modelzoo/instance_segmentation/yolov8n_seg/LICENSE.md |
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pipeline_tag: image-segmentation |
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--- |
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# Yolov8n_seg |
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## **Use case** : `Instance segmentation` |
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# Model description |
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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. |
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Yolov8n_seg is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter. |
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## Network information |
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| Network Information | Value | |
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|-------------------------|--------------------------------------| |
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| Framework | Tensorflow | |
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| Quantization | int8 | |
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| Paper | https://arxiv.org/pdf/2305.09972 | |
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## Recommended platform |
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| Platform | Supported | Recommended | |
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|----------|-----------|-------------| |
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| STM32L0 | [] | [] | |
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| STM32L4 | [] | [] | |
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| STM32U5 | [] | [] | |
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| STM32MP1 | [] | [] | |
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| STM32MP2 | [x] | [] | |
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| STM32N6| [x] | [x] | |
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--- |
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# Performances |
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## Metrics |
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Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option. |
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### Reference **NPU** memory footprint based on COCO dataset |
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|Model | Dataset | Format | Resolution | Series | Internal RAM (KiB)| External RAM (KiB)| Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version | |
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|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------| |
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| [Yolov8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_256_quant_pc_uf_seg_coco-st.tflite) | COCO | Int8 | 256x256x3 | STM32N6 | 2128 | 0.0 | 3425.39 | 10.0.0 | 2.0.0 |
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| [Yolov8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_320_quant_pc_uf_seg_coco-st.tflite) | COCO | Int8 | 320x320x3 | STM32N6 | 2564.06 | 0.0 | 3467.56 | 10.0.0 | 2.0.0 | |
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### Reference **NPU** inference time based on COCO Person dataset |
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| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version | |
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|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------| |
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| [YOLOv8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_256_quant_pc_uf_seg_coco-st.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 37.59 | 26.61 | 10.0.0 | 2.0.0 | |
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| [YOLOv8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_320_quant_pc_uf_seg_coco-st.tflite) | COCO-Person | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 53.21 | 18.79 | 10.0.0 | 2.0.0 | |
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## Retraining and Integration in a Simple Example |
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Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services). |
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For instance segmentation, the models are stored in the Ultralytics repository. You can find them at the following link: [Ultralytics YOLOv8-STEdgeAI Models](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/). |
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Please refer to the [Ultralytics documentation](https://docs.ultralytics.com/tasks/segment/#train) to retrain the model. |
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## References |
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<a id="1">[1]</a> 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](https://arxiv.org/abs/1405.0312) |
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<a id="2">[2]</a> Ultralytics, "YOLOv8: Next-Generation Object Detection and Segmentation Model." Ultralytics, 2023. [Link](https://github.com/ultralytics/ultralytics) |
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