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STMicroelectronics/
regnet_pt

image-classification

2003.13678

apache-2.0

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RegNet

Use case : `Image classification`

Model description

RegNet introduces a design space paradigm for neural networks. Rather than designing individual architectures, RegNet defines a design space of possible networks characterized by a few parameters, enabling systematic exploration of network designs.

The architecture uses quantized linear parameterization where networks are defined by simple equations, with systematic variation of width and depth patterns. RegNet employs group convolutions for efficiency, following a design space exploration methodology for finding optimal configurations.

RegNet is well-suited for research on neural network design principles, applications requiring systematic architecture selection, and scalable deployments with consistent design principles.

(source: https://arxiv.org/abs/2003.13678)

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

Network information

Network Information	Value
Framework	Torch
MParams	~2.55 M
Quantization	Int8
Provenance	https://github.com/facebookresearch/pycls
Paper	https://arxiv.org/abs/2003.13678

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
regnetx002_pt_224	Imagenet	Int8	224×224×3	STM32N6	1192.84	0	2606.72	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
regnetx002_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	9.96	100.40	3.0.0

Accuracy with Imagenet dataset

Model	Format	Resolution	Top 1 Accuracy
regnetx002_pt	Float	224x224x3	70.72 %
regnetx002_pt	Int8	224x224x3	68.95 %

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
regnetx002_pt	Float	224x224x3	70.72 %
regnetx002_pt	Int8	224x224x3	68.95 %

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: RegNet — https://github.com/facebookresearch/pycls

STMicroelectronics/regnet_pt