Release AI-ModelZoo-4.0.0

README.md

@@ -38,7 +38,7 @@ Yolov8n_seg is implemented in Pytorch by Ultralytics and is quantized in int8 fo
 # Performances
 
 ## Metrics
-Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
+Measures are done with default STEdgeAI Core version configuration with enabled input / output allocated option.
 > [!CAUTION] 
 > All YOLOv8 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
@@ -48,20 +48,29 @@ https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/
 
 ### 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_ii_seg_coco-st.tflite)  | COCO | Int8 | 256x256x3 | STM32N6 | 1119.06 | 0.0 | 3393.42 | 10.2.0 | 2.2.0 
-| [Yolov8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_320_quant_pc_ii_seg_coco-st.tflite)  | COCO | Int8 | 320x320x3 | STM32N6 | 1733.25 | 0.0 | 3435.34 | 10.2.0 | 2.2.0 |
+|Model      | Dataset       | Format   | Resolution | Series    | Internal RAM (KiB)| External RAM (KiB)| Weights Flash (KiB) | 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_ii_seg_coco-st.tflite)  | COCO | Int8 | 256x256x3 | STM32N6 | 855 | 0.0 | 3393.42 | 3.0.0 |
+| [Yolov8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_320_quant_pc_ii_seg_coco-st.tflite)  | COCO | Int8 | 320x320x3 | STM32N6 | 1413.89 | 0.0 | 3435.34 | 3.0.0 |
 
 
 
 ### 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 |
+| Model  | Dataset          | Format | Resolution  | Board            | Execution Engine | Inference time (ms) | Inf / sec   |  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_ii_seg_coco-st.tflite) | COCO-Person      | Int8   | 256x256x3  | STM32N6570-DK   |   NPU/MCU      |     33.64        |   29.72      |       10.2.0        |     2.2.0   |
-| [YOLOv8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_320_quant_pc_ii_seg_coco-st.tflite) | COCO-Person     | Int8    | 320x320x3  | STM32N6570-DK   |   NPU/MCU      |       43.8      |     22.83     |       10.2.0        |     2.2.0   |
+| [YOLOv8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_256_quant_pc_ii_seg_coco-st.tflite) | COCO-Person      | Int8   | 256x256x3  | STM32N6570-DK   |   NPU/MCU      |     31.57        |   29.72      |  3.0.0   |
+| [YOLOv8n seg per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_320_quant_pc_ii_seg_coco-st.tflite) | COCO-Person     | Int8    | 320x320x3  | STM32N6570-DK   |   NPU/MCU      |       41.87      |     22.83     |   3.0.0   |
 
+### Reference **MPU** inference time based on COCO 2017 Person dataset (instance segmentation)
 
+| Model                                                                                                                                                          | Dataset          | Format | Resolution | Quantization   | Board           | Execution Engine | Frequency | Inference time (ms) | %NPU  | %GPU | %CPU | X-LINUX-AI version | Framework         |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|--------|------------|----------------|-----------------|------------------|-----------|---------------------|-------|------|------|--------------------|-------------------|
+| [YOLOv8n-seg](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_256_quant_pc_ii_seg_coco-st.tflite) | person_coco_2017 | Int8   | 256x256x3  | per-channel\*\* | STM32MP257F-EV1 | NPU/GPU          | 800 MHz   | 19.84               | 91.71 | 8.29 | 0    | v6.1.0             | TensorFlow Lite   |
+| [YOLOv8n-seg](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/segmentation/yolov8n_320_quant_pc_ii_seg_coco-st.tflite) | person_coco_2017 | Int8   | 320x320x3  | per-channel\*\* | STM32MP257F-EV1 | NPU/GPU          | 800 MHz   | 30.97               | 93.59 | 6.41 | 0    | v6.1.0             | TensorFlow Lite   |
+
+** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
+
+** **Note:** On STM32MP2 devices, per-channel quantized models are internally converted to per-tensor quantization by the compiler using an entropy-based method. This may introduce a slight loss in accuracy compared to the original per-channel models.
 
 ## Retraining and Integration in a Simple Example
 Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services).
@@ -74,4 +83,4 @@ Please refer to the [Ultralytics documentation](https://docs.ultralytics.com/tas
 
 <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)
+<a id="2">[2]</a> Ultralytics, "YOLOv8: Next-Generation Object Detection and Segmentation Model." Ultralytics, 2023. [Link](https://github.com/ultralytics/ultralytics)