Release AI-ModelZoo-4.0.0

README.md

@@ -1,6 +1,74 @@
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-license: other
-license_name: sla0044
-license_link: >-
-  https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/instance_segmentation/LICENSE.md
----
+---
+license: other
+license_name: sla0044
+license_link: >-
+  https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/instance_segmentation/LICENSE.md
+---
+# Yolov11n_seg
+
+## **Use case** : `Instance segmentation`
+
+# Model description
+
+Yolov11n_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 Yolov11n_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.
+
+Yolov11n_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 | []       | []          |
+| STM32N6| [x]       | [x]          |
+
+---
+# Performances
+
+## Metrics
+Measures are done with default STEdgeAI Core version configuration with enabled input / output allocated option.
+> [!CAUTION] 
+> All YOLOv11 hyperlinks in the tables below link to an external GitHub folder, which is subject to its own license terms:
+https://github.com/stm32-hotspot/ultralytics/blob/main/LICENSE
+Please also check the folder's README.md file for detailed information about its use and content:
+https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/README.md
+
+
+### Reference **NPU** memory footprint based on COCO dataset
+
+|Model      | Dataset       | Format   | Resolution | Series    | Internal RAM (KiB)| External RAM (KiB)| Weights Flash (KiB) | STEdgeAI Core version |
+|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
+| [Yolov11n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov11n/yolov11n_256_quant_pc_ii_seg_coco80-st.tflite)  | COCO | Int8 | 256x256x3 | STM32N6 |   688.0 | 0.0 | 2569.38  | 3.0.0 
+
+
+
+### Reference **NPU**  inference time based on COCO Person dataset 
+| Model  | Dataset          | Format | Resolution  | Board            | Execution Engine | Inference time (ms) | Inf / sec   | STEdgeAI Core version |
+|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
+| [YOLOv11n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov11n/yolov11n_256_quant_pc_ii_seg_coco80-st.tflite) | COCO-Person      | Int8   | 256x256x3  | STM32N6570-DK   |   NPU/MCU      |     28.01      |   35.71    |    3.0.0   |
+
+
+
+## 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, "YOLO11: Next-Generation Object Detection and Segmentation Model." Ultralytics, 2023. [Link](https://github.com/ultralytics/ultralytics)