STMicroelectronics/ssd_mobilenetv2_pt

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
Paper	SSD MobileNetV2

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	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	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	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	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	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	VOC	Int8	300x300x3	STM32N6570-DK	NPU/MCU	135.06	7.40	3.0.0

AP on COCO dataset

Dataset details: link , License CC BY 4.0, Number of classes: 80

Model	Format	Resolution	AP50
ssd_mobilenetv2_pt	Float	3x300x300	31.75
ssd_mobilenetv2_pt	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 , License CC BY 4.0 , Number of classes: 1

Model	Format	Resolution	AP50
ssd_mobilenetv2_pt	Float	3x300x300	41.91
ssd_mobilenetv2_pt	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 , License , Number of classes: 20

Model	Format	Resolution	AP50
ssd_mobilenetv2_pt	Float	3x300x300	67.03
ssd_mobilenetv2_pt	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

Datasets

• COCO
  Lin et al., Microsoft COCO: Common Objects in Context

• PASCAL VOC
  Everingham et al., The PASCAL Visual Object Classes (VOC) Challenge

References

• SSD
  Liu et al., SSD: Single Shot MultiBox Detector

• MobileNetV2
  Sandler et al., MobileNetV2: Inverted Residuals and Linear Bottlenecks