You can deploy this model and start earning money today!

STMicroelectronics/
mnasnet_pt

image-classification

1807.11626

apache-2.0

Views

No views yet

MnasNet

Use case : `Image classification`

Model description

Mobile Neural Architecture Search Network (MnasNet) is designed using automated neural architecture search (NAS) specifically targeting mobile devices. It optimizes for both accuracy and real-device latency simultaneously.

MnasNet employs multi-objective optimization to balance accuracy with latency on target devices, using inverted residual blocks similar to MobileNetV2 but with NAS-optimized configurations. The factorized hierarchical search space enables diverse and efficient architectures.

The architecture is well-suited for mobile and embedded vision applications, particularly in scenarios requiring optimized accuracy-latency trade-offs.

(source: https://arxiv.org/abs/1807.11626)

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

Network information

Network Information	Value
Framework	Torch
MParams	~2.27 M
Quantization	Int8
Provenance	https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper	https://arxiv.org/abs/1807.11626

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
mnasnet_d050_pt_224	Imagenet	Int8	224×224×3	STM32N6	612.5	0	2319.53	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
mnasnet_d050_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	11.21	89.21	3.0.0

Accuracy with Imagenet dataset

Model	Format	Resolution	Top 1 Accuracy
mnasnet_d050_pt	Float	224x224x3	67.50 %
mnasnet_d050_pt	Int8	224x224x3	59.99 %

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
mnasnet_d050_pt	Float	224x224x3	67.50 %
mnasnet_d050_pt	Int8	224x224x3	59.99 %

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: MnasNet — https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet

STMicroelectronics/mnasnet_pt