You can deploy this model and start earning money today!

STMicroelectronics/
mobilenetv4_pt

Name: mobilenetv4_pt
Availability: InStock

image-classification

2404.10518

apache-2.0

Views

No views yet

MobileNet V4

Use case : `Image classification`

Model description

MobileNetV4 represents the latest evolution in the MobileNet family, introducing Universal Inverted Bottleneck (UIB) blocks that unify various efficient convolution designs. It achieves state-of-the-art accuracy-efficiency trade-offs on mobile hardware.

The architecture features a flexible UIB block design accommodating various operations, optimized through Neural Architecture Search for multiple hardware platforms. It includes Mobile MQA Attention as an efficient attention mechanism for mobile deployment, providing enhanced feature extraction with improved capacity per FLOP.

MobileNetV4 is ideal for state-of-the-art mobile vision applications requiring the latest architectural improvements, though it shows quantization sensitivity (~10% drop) that should be considered for INT8 deployment.

(source: https://arxiv.org/abs/2404.10518)

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

Network information

Network Information	Value
Framework	Torch
MParams	~3.67 M
Quantization	Int8
Provenance	https://github.com/huggingface/pytorch-image-models
Paper	https://arxiv.org/abs/2404.10518

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
mobilenetv4small_pt_224	Imagenet	Int8	224×224×3	STM32N6	539	0	3760.53	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
mobilenetv4small_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	13.74	72.78	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
mobilenetv4small_pt	Float	224x224x3	74.33 %
mobilenetv4small_pt	Int8	224x224x3	64.24 %

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: MobileNetV4 — https://arxiv.org/abs/2404.10518

STMicroelectronics/mobilenetv4_pt