--- license: other license_name: sla0044 license_link: >- https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/LICENSE.md pipeline_tag: object-detection --- # **SSD MobileNetV2** ## **Use case** : `Object detection` ## **Model description** SSD MobileNetV2 is a **single-shot object detection model** designed for **efficient and low-latency inference** on resource-constrained devices such as mobile and edge platforms. The model combines the **Single Shot Detector (SSD)** framework with **MobileNetV2** as the backbone network. MobileNetV2 employs **inverted residual blocks and linear bottlenecks**, enabling a strong balance between accuracy and computational efficiency. The SSD head performs object localization and classification in a **single forward pass**, making the model suitable for real-time detection scenarios. The `ssd_mobilenetv2_pt` variant is implemented in **PyTorch** and is commonly used as a lightweight baseline for object detection tasks where **speed, memory footprint, and power efficiency** are critical. ## **Network information** | Network information | Value | |--------------------|-------| | Framework | Torch | | Quantization | Int8 | | Provenance | [torchvision GitHub](https://github.com/pytorch/vision) | | Paper | [SSD](https://arxiv.org/abs/1512.02325)
[MobileNetV2](https://arxiv.org/abs/1801.04381) | The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment. ## Network inputs / outputs For an image resolution of NxM and NC classes | Input Shape | Description | | ----- | ----------- | | (1, W, H, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | | Output Shape | Description | | ----- | ----------- | | (1, 3000,(1+NC) and (1,3000,4)) | Model returns two output vectors of bounding boxes where first output returns confidence for each class (+ background class) and second output returns bounding box coordinates (x1, y1, x2,y2) | ## Recommended Platforms | Platform | Supported | Recommended | |----------|-----------|-------------| | STM32L0 | [] | [] | | STM32L4 | [] | [] | | STM32U5 | [] | [] | | STM32H7 | [] | [] | | STM32MP1 | [] | [] | | STM32MP2 | [] | [] | | STM32N6 | [x] | [x] | # Performances ## Metrics Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option. ### Reference **NPU** memory footprint based on COCO dataset (see Accuracy for details on dataset) | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |-------|---------|--------|------------|--------|-------------------|-------------------|--------------------|-----------------------| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300_qdq_int8.onnx) | COCO | Int8 | 300x300x3 | STM32N6 | 2323.25 | 2109.38 | 20033.69 | 3.0.0 | ### Reference **NPU** inference time based on COCO dataset (see Accuracy for details on dataset) | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |-------|---------|--------|------------|-------|------------------|--------------------|-----------|-----------------------| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300_qdq_int8.onnx) | COCO | Int8 | 300x300x3 | STM32N6570-DK | NPU/MCU | 158.49 | 6.31 | 3.0.0 | ### 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 | |-------|---------|--------|------------|--------|-------------------|-------------------|--------------------|-----------------------| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300_qdq_int8.onnx) | COCO-Person | Int8 | 300x300x3 | STM32N6 | 2182.72 | 2109.38 | 8005.94 | 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 | |-------|---------|--------|------------|-------|------------------|--------------------|-----------|-----------------------| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300_qdq_int8.onnx) | COCO-Person | Int8 | 300x300x3 | STM32N6570-DK | NPU/MCU | 126.19 | 7.92 | 3.0.0 | ### Reference **NPU** memory footprint based on VOC dataset (see Accuracy for details on dataset) | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |-------|---------|--------|------------|--------|-------------------|-------------------|--------------------|-----------------------| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300_qdq_int8.onnx) | VOC | Int8 | 300x300x3 | STM32N6 | 2237.00 | 2109.38 | 10898.69 | 3.0.0 | ### Reference **NPU** inference time based on VOC dataset (see Accuracy for details on dataset) | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |-------|---------|--------|------------|-------|------------------|--------------------|-----------|-----------------------| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300_qdq_int8.onnx) | VOC | Int8 | 300x300x3 | STM32N6570-DK | NPU/MCU | 135.06 | 7.40 | 3.0.0 | ### AP on COCO dataset Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode), Number of classes: 80 | Model | Format | Resolution | AP50 | | --- | --- | --- | --- | | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300.onnx) | Float | 3x300x300 | 31.75 | | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300_qdq_int8.onnx) | Int8 | 3x300x300 | 31.29 | \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100 ### 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) , Number of classes: 1 | Model | Format | Resolution | AP50 | | --- | --- | --- | --- | | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300.onnx) | Float | 3x300x300 | 41.91 | | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300_qdq_int8.onnx) | Int8 | 3x300x300 | 41.74 | \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100 ### AP on VOC dataset Dataset details: [link](https://www.robots.ox.ac.uk/~vgg/projects/pascal/VOC/) , [License](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/HTML/license.html) , Number of classes: 20 | Model | Format | Resolution | AP50 | | --- | --- | --- | --- | | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300.onnx) | Float | 3x300x300 | 67.03 | | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300_qdq_int8.onnx) | Int8 | 3x300x300 | 66.91 | \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100 ## Retraining and Integration in a simple example: Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) ## Datasets - **COCO** [Lin et al., *Microsoft COCO: Common Objects in Context*](https://arxiv.org/abs/1405.0312) - **PASCAL VOC** [Everingham et al., *The PASCAL Visual Object Classes (VOC) Challenge*](http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.pdf) ## References - **SSD** [Liu et al., *SSD: Single Shot MultiBox Detector*](https://arxiv.org/abs/1512.02325) - **MobileNetV2** [Sandler et al., *MobileNetV2: Inverted Residuals and Linear Bottlenecks*](https://arxiv.org/abs/1801.04381)