| --- |
| license: other |
| license_name: sla0044 |
| license_link: >- |
| https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/main/object_detection/yolov11n/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. |
| > [!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 (KiB) | External RAM (KiB) | Weights Flash (KiB) | 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 | 270 | 0 | 2936.52 | 3.0.0 | |
| | [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 | 624 | 0 | 2959.08 | 3.0.0 | |
| | [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 | 758.31 | 0 | 2963.51 | 3.0.0 | |
| | [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 | 2067.88 | 0 | 2972.00 | 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 | |
| |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|--------------|---------------|--------------------|-----------------------|-------------|-------------------------| |
| | [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 | 16.25 | 61.54 | 3.0.0 | |
| | [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 | 23.84 | 41.96 | 3.0.0 | |
| | [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 | 32.26 | 31.00 | 3.0.0 | |
| | [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 | 54.64 | 18.30 | 3.0.0 | |
| |
| |
| ### Reference **MCU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset) |
| |
| | Model | Format | Resolution | Series | Activation RAM (KiB) | Runtime RAM (KiB) | Weights Flash (KiB) | Code Flash (KiB) | Total RAM (KiB) | Total Flash (KiB) | 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) | Int8 | 192x192x3 | STM32H7 | 367.69 | 62.11 | 2955.64 | 151.16 | 429.80 | 3106.80 | 3.0.0 | |
| | [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) | Int8 | 256x256x3 | STM32H7 | 653.67 | 62.11 | 2959.08 | 151.29 | 715.78 | 3110.37 | 3.0.0 | |
| | [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) | Int8 | 320x320x3 | STM32H7 | 1021.36 | 62.11 | 2963.51 | 151.41 | 1083.47 | 3114.92 | 3.0.0 | |
| | [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) | Int8 | 416x416x3 | STM32H7 | 1726.09 | 62.11 | 2972.00 | 151.33 | 1788.20 | 3123.33 | 3.0.0 | |
| |
| |
| ### Reference **MCU** inference time based on COCO Person dataset (see Accuracy for details on dataset) |
| |
| | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | 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) | Int8 | 192x192x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 1612.46 | 3.0.0 | |
| | [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) | Int8 | 256x256x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 2839.27 | 3.0.0 | |
| | [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) | Int8 | 320x320x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 4442.59 | 3.0.0 | |
| | [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) | Int8 | 416x416x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 7868.62 | 3.0.0 | |
| |
| ### 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 | 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 | 53.58 % | |
| | [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 | 61.86 % | |
| | [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 | 64.79 % | |
| |
| \* 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). |
| 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/detect/#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} |
| } |
