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STMicroelectronics/fdmobilenet_pt

sourceHugging Faceapache-2.0updated 8mo agoView on Hugging Face

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Model Card

FDMobileNet

Use case : `Image classification`

Model description

Fast-Downsampling MobileNet (FDMobileNet) is an optimized variant of MobileNet designed for extremely fast inference. It achieves speed improvements through aggressive early spatial reduction while maintaining reasonable accuracy.

FDMobileNet employs a fast downsampling strategy that reduces spatial dimensions early in the network to minimize computation. It retains depthwise separable convolutions inherited from MobileNet for parameter efficiency, and uses a width multiplier (Alpha) to scale the number of channels (a025 = 0.25x, a050 = 0.5x, a075 = 0.75x).

Among the fastest models in the model zoo, FDMobileNet is ideal for ultra-low-latency real-time applications and battery-powered devices with strict power constraints.

(source: https://arxiv.org/abs/1802.03750)

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

Network information

Network Information	Value
Framework	Torch
MParams	~0.37–1.77 M
Quantization	Int8
Provenance	https://github.com/qinzheng93/FD-MobileNet
Paper	https://arxiv.org/abs/1802.03750

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)	Weights Flash (KiB)	STEdgeAI Core version
fdmobilenet_a025_pt_224	Imagenet	Int8	224×224×3	STM32N6	294	377.03	3.0.0
fdmobilenet_a050_pt_224	Imagenet	Int8	224×224×3	STM32N6	343	973.39	3.0.0
fdmobilenet_a075_pt_224	Imagenet	Int8	224×224×3	STM32N6	441	1813.66	3.0.0

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

Model	Dataset	Format	Resolution	Board	Execution Engine	Inference time (ms)	Inf / sec	STEdgeAI Core version
fdmobilenet_a025_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	1.88	531.91	3.0.0
fdmobilenet_a050_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	4.07	245.70	3.0.0
fdmobilenet_a075_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	6.83	146.41	3.0.0

Accuracy with Imagenet dataset

Model	Format	Resolution	Top 1 Accuracy
fdmobilenet_a025_pt	Float	224x224x3	45.37 %
fdmobilenet_a025_pt	Int8	224x224x3	29.73 %
fdmobilenet_a050_pt	Float	224x224x3	58.04 %
fdmobilenet_a050_pt	Int8	224x224x3	41.58 %
fdmobilenet_a075_pt	Float	224x224x3	62.10 %
fdmobilenet_a075_pt	Int8	224x224x3	60.29 %

Model	Format	Resolution	Top 1 Accuracy
fdmobilenet_a025_pt	Float	224x224x3	45.37 %
fdmobilenet_a025_pt	Int8	224x224x3	29.73 %
fdmobilenet_a050_pt	Float	224x224x3	58.04 %
fdmobilenet_a050_pt	Int8	224x224x3	41.58 %
fdmobilenet_a075_pt	Float	224x224x3	62.10 %
fdmobilenet_a075_pt	Int8	224x224x3	60.29 %

Retraining and Integration in a simple example:

Please refer to the stm32ai-modelzoo-services GitHub here

References

<a id="1">[1]</a> - Dataset: Imagenet (ILSVRC 2012) — https://www.image-net.org/

<a id="2">[2]</a> - Model: FD-MobileNet — https://arxiv.org/abs/1802.03750