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README.md

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-license: other
-license_name: sla0044
-license_link: >-
-  https://github.com/STMicroelectronics/stm32ai-modelzoo/instance_segmentation/yolov8n_seg/LICENSE.md
----
+---
+license: other
+license_name: sla0044
+license_link: >-
+  https://github.com/STMicroelectronics/stm32ai-modelzoo/instance_segmentation/yolov8n_seg/LICENSE.md
+pipeline_tag: image-segmentation
+---
+# 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](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 
+| [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 |
+
+
+
+### 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](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   |
+| [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   |
+
+
+
+## Retraining and Integration in a Simple Example
+Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services).
+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/).
+
+Please refer to the [Ultralytics documentation](https://docs.ultralytics.com/tasks/segment/#train) to retrain the model.
+
+
+## References
+
+<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)
+
+<a id="2">[2]</a> Ultralytics, "YOLOv8: Next-Generation Object Detection and Segmentation Model." Ultralytics, 2023. [Link](https://github.com/ultralytics/ultralytics)
+
+