| | --- |
| | license: other |
| | license_name: sla0044 |
| | license_link: >- |
| | 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 |
| |
|
| |
|
| | | 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 | [] | [] | |
| | | STM32N6 | [x] | [x] | |
| |
|
| |
|
| | # Performances |
| |
|
| | ## 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). |
| | 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} |
| | } |
