Update README.md

README.md

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----
-license: other
-license_name: sla0044
-license_link: >-
-  https://github.com/STMicroelectronics/stm32aimodelzoo/object_detection/yolov8n/LICENSE.md
----
+---
+license: other
+license_name: sla0044
+license_link: >-
+  https://github.com/STMicroelectronics/stm32aimodelzoo/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
+
+
+| Network information     |  Value          |
+|-------------------------|-----------------|
+|  Framework              | TensorFlow Lite |
+|  Quantization           | int8            |
+|  Provenance             | https://docs.ultralytics.com/tasks/detect/ |
+
+
+## Networks inputs / outputs
+
+With an image resolution of NxM and K classes to detect:
+
+| Input Shape | Description |
+| ----- | ----------- |
+| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 |
+
+| Output Shape | Description |
+| ----- | ----------- |
+| (1, 4+K, F) | FLOAT values Where F = (N/8)^2 + (N/16)^2 + (N/32)^2 is the 3 concatenated feature maps |
+
+
+## Recommended Platforms
+
+
+| Platform | Supported | Recommended |
+|----------|-----------|-------------|
+| STM32L0  | []        | []          |
+| STM32L4  | []        | []          |
+| STM32U5  | []        | []          |
+| STM32H7  | []        | []          |
+| STM32MP1 | []        | []          |
+| STM32MP2 | [x]       | [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 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   |  697.5  | 0.0 | 2965.61 | 10.0.0 | 2.0.0 |
+| [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   | 1626 | 0.0 | 2970.13 | 10.0.0 | 2.0.0 |
+| [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   | 2162.5 | 0.0 | 2975.99 | 10.0.0 | 2.0.0 |
+| [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   | 2704 | 0.0 | 2987.52 | 10.0.0 | 2.0.0 |
+
+
+### 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   | 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      | 18.91 | 52.89 | 10.0.0 | 2.0.0 |
+| [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      | 28.6 | 34.97 | 10.0.0 | 2.0.0 |
+| [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      | 38.32 | 26.09 |       10.0.0        |     2.0.0   |
+| [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      | 63.03 | 15.86 |       10.0.0        |     2.0.0   |
+
+
+### 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     | v5.0.0             | 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     | v5.0.0             | 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  | 56.90 %  |
+| [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  | 62.60 %  |
+| [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  | 66.20 %  |
+| [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  | 68.90 %  |
+
+\* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
+
+## 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).
+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/pose/#train) to retrain the models.
+
+# References
+
+<a id="1">[1]</a>
+“Microsoft COCO: Common Objects in Context”. [Online]. Available: https://cocodataset.org/#download.
+@article{DBLP:journals/corr/LinMBHPRDZ14,
+  author    = {Tsung{-}Yi Lin and
+               Michael Maire and
+               Serge J. Belongie and
+               Lubomir D. Bourdev and
+               Ross B. Girshick and
+               James Hays and
+               Pietro Perona and
+               Deva Ramanan and
+               Piotr Doll{'{a} }r and
+               C. Lawrence Zitnick},
+  title     = {Microsoft {COCO:} Common Objects in Context},
+  journal   = {CoRR},
+  volume    = {abs/1405.0312},
+  year      = {2014},
+  url       = {http://arxiv.org/abs/1405.0312},
+  archivePrefix = {arXiv},
+  eprint    = {1405.0312},
+  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+