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STMicroelectronics/
ssdlite_mobilenetv3small_pt

Name: ssdlite_mobilenetv3small_pt
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SSDLite MobileNetV3 Small

Use case : `Object detection`

Model description

SSDLite MobileNetV3 Small is a lightweight single-shot object detection model optimized for real-time inference on mobile and edge devices with minimal computational resources.

It combines the SSDLite framework with MobileNetV3 Small as the backbone. MobileNetV3 Small introduces efficient inverted residual blocks, lightweight attention (SE modules), and hard-swish activations, enabling a strong balance between accuracy, speed, and efficiency.
The SSDLite head predicts object locations and class probabilities in a single forward pass, making the model suitable for real-time detection on resource-constrained platforms such as mobile devices and embedded systems.

The ssdlite_mobilenetv3small_pt variant is implemented in PyTorch and is widely used in scenarios where ultra-low latency and minimal memory footprint are critical.

Network information

Network information	Value
Framework	Torch
Quantization	Int8
Provenance	torchvision GitHub
Paper	SSDLite MobileNetV3

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
ssdlite_mobilenetv3small_pt	COCO	Int8	300x300x3	STM32N6	1763.09	0	1978.72	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
ssdlite_mobilenetv3small_pt	COCO	Int8	300x300x3	STM32N6570-DK	NPU/MCU	15.86	63.05	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
ssdlite_mobilenetv3small_pt	COCO-Person	Int8	300x300x3	STM32N6	1764.33	0	1186.25	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
ssdlite_mobilenetv3small_pt	COCO-Person	Int8	300x300x3	STM32N6570-DK	NPU/MCU	13.73	72.83	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
ssdlite_mobilenetv3small_pt	VOC	Int8	300x300x3	STM32N6	1764.33	0	1376.84	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
ssdlite_mobilenetv3small_pt	VOC	Int8	300x300x3	STM32N6570-DK	NPU/MCU	14.30	69.93	3.0.0

AP on COCO dataset

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

Model	Format	Resolution	AP50
ssdlite_mobilenetv3small_pt	Float	3x300x300	20.13
ssdlite_mobilenetv3small_pt	Int8	3x300x300	18.85

* 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
ssdlite_mobilenetv3small_pt	Float	3x300x300	35.19
ssdlite_mobilenetv3small_pt	Int8	3x300x300	31.87

* 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
ssdlite_mobilenetv3small_pt	Float	3x300x300	55.56
ssdlite_mobilenetv3small_pt	Int8	3x300x300	54.58

* 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

References

COCO
Lin et al., Microsoft COCO: Common Objects in Context
PASCAL VOC
Everingham et al., The PASCAL Visual Object Classes (VOC) Challenge
SSDLite
Howard et al., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (SSDLite section)
MobileNetV3 Small
Howard et al., Searching for MobileNetV3

STMicroelectronics/ssdlite_mobilenetv3small_pt

SSDLite MobileNetV3 Small

Use case : Object detection

Model description

Network information

Network inputs / outputs

Recommended Platforms

Performances

Metrics

Reference NPU memory footprint based on COCO dataset (see Accuracy for details on dataset)

Reference NPU inference time based on COCO dataset (see Accuracy for details on dataset)

Reference NPU memory footprint based on COCO Person dataset (see Accuracy for details on dataset)

Reference NPU inference time based on COCO Person dataset (see Accuracy for details on dataset)

Reference NPU memory footprint based on VOC dataset (see Accuracy for details on dataset)

Reference NPU inference time based on VOC dataset (see Accuracy for details on dataset)

AP on COCO dataset

AP on COCO-Person dataset

AP on VOC dataset

Retraining and Integration in a simple example:

References

STMicroelectronics/
ssdlite_mobilenetv3small_pt

Use case : `Object detection`