STMicroelectronics
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yolov11n

Object Detection

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FBAGSTM commited on Jan 22

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Release AI-ModelZoo-4.0.0

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@@ -5,15 +5,15 @@ license_link: >-
   https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/main/object_detection/yolov8n/LICENSE.md
 pipeline_tag: object-detection
 ---
-# Yolov8n object detection quantized
 ## **Use case** : `Object detection`
 # Model description
-Yolov8n is a lightweight and efficient object detection 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 is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter.
 ## Network information
@@ -48,7 +48,7 @@ With an image resolution of NxM and K classes to detect:
 | STM32U5  | []        | []          |
 | STM32H7  | []        | []          |
 | STM32MP1 | []        | []          |
-| STM32MP2 | [x]       | [x]         |
 | STM32N6  | [x]       | [x]         |
@@ -56,51 +56,35 @@ With an image resolution of NxM and K classes to detect:
 ## Metrics
-Measures are done with default STM32Cube.AI 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
 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 Person dataset (see Accuracy for details on dataset)
-| Model                                                                                                                                                                                 | Dataset     | Format   | Resolution   | Series   |   Internal RAM |   External RAM |   Weights Flash | STM32Cube.AI version   | STEdgeAI Core version   |
-|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|--------------|----------|----------------|----------------|-----------------|------------------------|-------------------------|
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_192_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 192x192x3    | STM32N6  |         261    |              0 |         2936.52 | 10.2.0                 | 2.2.0                   |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_256_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 256x256x3    | STM32N6  |         624    |              0 |         2941.09 | 10.2.0                 | 2.2.0                   |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_320_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 320x320x3    | STM32N6  |         839.06 |              0 |         2947.02 | 10.2.0                 | 2.2.0                   |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_416_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 416x416x3    | STM32N6  |        2242.84 |              0 |         2958.34 | 10.2.0                 | 2.2.0                   |
 ### Reference **NPU**  inference time based on COCO Person dataset (see Accuracy for details on dataset)
-| Model                                                                                                                                                                                 | Dataset     | Format   | Resolution   | Board         | Execution Engine   |   Inference time (ms) |   Inf / sec | STM32Cube.AI version   | STEdgeAI Core version   |
-|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|--------------|---------------|--------------------|-----------------------|-------------|------------------------|-------------------------|
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_192_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 192x192x3    | STM32N6570-DK | NPU/MCU            |                 16.88 |       59.24 | 10.2.0                 | 2.2.0                   |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_256_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 256x256x3    | STM32N6570-DK | NPU/MCU            |                 24.94 |       40.1  | 10.2.0                 | 2.2.0                   |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_320_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 320x320x3    | STM32N6570-DK | NPU/MCU            |                 31.75 |       31.5  | 10.2.0                 | 2.2.0                   |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_416_quant_pc_uf_od_coco-person.tflite) | COCO-Person | Int8     | 416x416x3    | STM32N6570-DK | NPU/MCU            |                 53.11 |       18.83 | 10.2.0                 | 2.2.0                   |
-### Reference **MPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
- Model         | Format | Resolution | Quantization  | Board             | Execution Engine | Frequency | Inference time (ms) | %NPU  | %GPU  | %CPU | X-LINUX-AI version |       Framework       |
-|-----------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_256_quant_pc_uf_pose_coco-st.tflite) | Int8   | 256x256x3  |  per-channel**  | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 102.8 ms            | 11.70  | 88.30 |0     | v6.1.0             | OpenVX                |
-| [YOLOv8n per tensor](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_256_quant_pt_uf_pose_coco-st.tflite)  | Int8   | 256x256x3  |  per-tensor     | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 17.57 ms            | 86.79  | 13.21 |0     | v6.1.0             | OpenVX                |
-** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
 ### AP on COCO Person dataset
 Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) , Quotation[[1]](#1) , Number of classes: 80, Number of images: 118,287
 | Model | Format | Resolution |       AP*   |
 |-------|--------|------------|----------------|
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_192_quant_pc_uf_od_coco-person.tflite) | Int8   | 192x192x3  | 53.50 %  |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_256_quant_pc_uf_od_coco-person.tflite) | Int8   | 256x256x3  | 58.40 %  |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_320_quant_pc_uf_od_coco-person.tflite) | Int8   | 320x320x3  | 61.80 %  |
-| [YOLOv8n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolov8n_416_quant_pc_uf_od_coco-person.tflite) | Int8   | 416x416x3  | 64.80 %  |
 \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100
 ## Integration in a simple example and other services support:
 Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services).

   https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/main/object_detection/yolov8n/LICENSE.md
 pipeline_tag: object-detection
 ---
+# Yolo11n object detection quantized
 ## **Use case** : `Object detection`
 # Model description
+Yolo11n is a lightweight and efficient object detection 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 Yolo11n 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.
+Yolo11n is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter.
 ## Network information
 | STM32U5  | []        | []          |
 | STM32H7  | []        | []          |
 | STM32MP1 | []        | []          |
+| STM32MP2 | []        | []         |
 | STM32N6  | [x]       | [x]         |
 ## Metrics
+Measures are done with default STM32Cube.AI 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 Person dataset (see Accuracy for details on dataset)
+| Model                                                                                                                                                                                           | Dataset     | Format   | Resolution   | Series   |   Internal RAM |   External RAM |   Weights Flash | STEdgeAI Core version   |
+|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|--------------|----------|----------------|----------------|-----------------|-------------------------|
+| [YOLO11n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolo11/yolo11n_256_quant_pc_uf_od_coco-person-st.tflite) | COCO-Person | Int8     | 256x256x3    | STM32N6  |         656 |              0 |         2535.83 | 3.0.0                   |
 ### Reference **NPU**  inference time based on COCO Person dataset (see Accuracy for details on dataset)
+| Model                                                                                                                                                                                           | Dataset     | Format   | Resolution   | Board         | Execution Engine   |   Inference time (ms) |   Inf / sec | STEdgeAI Core version   |
+|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|--------------|---------------|--------------------|-----------------------|-------------|-------------------------|
+| [YOLO11n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolo11/yolo11n_256_quant_pc_uf_od_coco-person-st.tflite) | COCO-Person | Int8     | 256x256x3    | STM32N6570-DK | NPU/MCU            |                 26.37 |       36.50 | 3.0.0                   |
 ### AP on COCO Person dataset
 Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) , Quotation[[1]](#1) , Number of classes: 80, Number of images: 118,287
 | Model | Format | Resolution |       AP*   |
 |-------|--------|------------|----------------|
+| [YOLOv11n per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/object_detection/yolo11/yolo11n_256_quant_pc_uf_od_coco-person-st.tflite) | Int8   | 640x640x3  | 64 %  |
 \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100
 ## Integration in a simple example and other services support:
 Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services).