STMicroelectronics/mobilenet_pt

MobileNet V1

Use case : Image classification

Model description

The original MobileNet architecture pioneered the use of depthwise separable convolutions for efficient mobile vision. It dramatically reduces computation and model size while maintaining competitive accuracy.

MobileNet factorizes standard convolutions into depthwise and pointwise operations, dramatically reducing computational cost. The architecture supports a width multiplier (Alpha) to scale channel dimensions (a025 = 0.25x, a050 = 0.5x, a075 = 0.75x), and uses linear bottleneck for efficient channel expansion and compression.

Resolution multipliers can further scale input resolution for additional efficiency, making MobileNet ideal for real-time mobile applications and resource-constrained embedded systems.

(source: https://arxiv.org/abs/1704.04861)

The model is quantized to int8 using ONNX Runtime and exported for efficient deployment.

Network information

Network Information	Value
Framework	Torch
MParams	~0.46–2.55 M
Quantization	Int8
Provenance	https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
Paper	https://arxiv.org/abs/1704.04861

Network inputs / outputs

For an image resolution of NxM and P classes

Input Shape	Description
(1, N, M, 3)	Single NxM RGB image with UINT8 values between 0 and 255

Output Shape	Description
(1, P)	Per-class confidence for P classes in FLOAT32

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.
• All the models are trained from scratch on Imagenet dataset

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

Model	Dataset	Format	Resolution	Series	Internal RAM (KiB)	External RAM (KiB)	Weights Flash (KiB)	STEdgeAI Core version
mobilenet_a025_pt_224	Imagenet	Int8	224×224×3	STM32N6	392	0	469.45	3.0.0
mobilenet_a050_pt_224	Imagenet	Int8	224×224×3	STM32N6	588	0	1318.3	3.0.0
mobilenet_a075_pt_224	Imagenet	Int8	224×224×3	STM32N6	1323	0	2612.79	3.0.0
mobilenetb_a025_pt_224	Imagenet	Int8	224×224×3	STM32N6	392	0	469.3	3.0.0
mobilenetb_a050_pt_224	Imagenet	Int8	224×224×3	STM32N6	588	0	1317.91	3.0.0
mobilenetb_a075_pt_224	Imagenet	Int8	224×224×3	STM32N6	1323	0	2602.29	3.0.0

Reference NPU inference time and imagenet dataset (see Accuracy for details on dataset)

Model	Dataset	Format	Resolution	Board	Execution Engine	Inference time (ms)	Inf / sec	STEdgeAI Core version
mobilenet_a025_pt_224	Imagenet	Int8	224x224x3	STM32N6570-DK	NPU/MCU	2.98	335.57	3.0.0
mobilenet_a050_pt_224	Imagenet	Int8	224x224x3	STM32N6570-DK	NPU/MCU	6.55	152.67	3.0.0
mobilenet_a075_pt_224	Imagenet	Int8	224x224x3	STM32N6570-DK	NPU/MCU	11.73	85.25	3.0.0
mobilenetb_a025_pt_224	Imagenet	Int8	224x224x3	STM32N6570-DK	NPU/MCU	2.89	345.90	3.0.0
mobilenetb_a050_pt_224	Imagenet	Int8	224x224x3	STM32N6570-DK	NPU/MCU	6.65	150.38	3.0.0
mobilenetb_a075_pt_224	Imagenet	Int8	224x224x3	STM32N6570-DK	NPU/MCU	11.70	85.47	3.0.0

Accuracy with Imagenet dataset

Dataset details: link Number of classes: 1000. To perform the quantization, we calibrated the activations with a random subset of the training set. For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set.

Model	Format	Resolution	Top 1 Accuracy
mobilenet_a025_pt	Float	224x224x3	54.81 %
mobilenet_a025_pt	Int8	224x224x3	50.55 %
mobilenet_a050_pt	Float	224x224x3	66.60 %
mobilenet_a050_pt	Int8	224x224x3	64.37 %
mobilenet_a075_pt	Float	224x224x3	71.01 %
mobilenet_a075_pt	Int8	224x224x3	69.91 %
mobilenetb_a025_pt	Float	224x224x3	55.53 %
mobilenetb_a025_pt	Int8	224x224x3	53.81 %
mobilenetb_a050_pt	Float	224x224x3	67.44 %
mobilenetb_a050_pt	Int8	224x224x3	65.96 %
mobilenetb_a075_pt	Float	224x224x3	71.46 %
mobilenetb_a075_pt	Int8	224x224x3	69.72 %

Retraining and Integration in a simple example:

Please refer to the stm32ai-modelzoo-services GitHub here

References

[1] - Dataset: Imagenet (ILSVRC 2012) — https://www.image-net.org/

[2] - Model: MobileNets — https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md