STMicroelectronics/sqnxt_pt

SqueezeNext

Use case : Image classification

Model description

SqueezeNext is the successor to SqueezeNet, offering improved accuracy through skip connections, bottleneck modules, and separable convolutions. It is specifically designed for hardware efficiency.

The architecture employs a two-stage bottleneck with 1x1 squeeze followed by 1x1-3x3 expand patterns, with skip connections added for improved gradient flow. Separable convolutions further reduce computational cost, and the hardware-aware design is optimized for specific hardware platforms.

SqueezeNext is ideal for applications requiring SqueezeNet-style compactness with better accuracy, and hardware platforms with specific optimization targets.

(source: https://arxiv.org/abs/1803.10615)

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

Network information

Network Information	Value
Framework	Torch
MParams	~0.68–3.17 M
Quantization	Int8
Provenance	https://github.com/amirgholami/SqueezeNext
Paper	https://arxiv.org/abs/1803.10615

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
sqnxt23_x100_pt_224	Imagenet	Int8	224×224×3	STM32N6	2086.45	3025	693.67	3.0.0
sqnxt23_x150_pt_224	Imagenet	Int8	224×224×3	STM32N6	2087.48	6806.25	1453.99	3.0.0
sqnxt23_x200_pt_224	Imagenet	Int8	224×224×3	STM32N6	2275.52	9075	2493.33	3.0.0
sqnxt23v5_x150_pt_224	Imagenet	Int8	224×224×3	STM32N6	2087.48	6806.25	1879.24	3.0.0
sqnxt23v5_x200_pt_224	Imagenet	Int8	224×224×3	STM32N6	2275.52	9075	3249.45	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
sqnxt23_x100_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	87.07	11.49	3.0.0
sqnxt23_x150_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	127.46	7.85	3.0.0
sqnxt23_x200_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	182.12	5.49	3.0.0
sqnxt23v5_x100_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	86.37	11.58	3.0.0
sqnxt23v5_x150_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	126.91	7.88	3.0.0
sqnxt23v5_x200_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	181.01	5.52	3.0.0

Accuracy with Imagenet dataset

Model	Format	Resolution	Top 1 Accuracy
sqnxt23_x100_pt	Float	224x224x3	58.18 %
sqnxt23_x100_pt	Int8	224x224x3	57.86 %
sqnxt23_x150_pt	Float	224x224x3	66.17 %
sqnxt23_x150_pt	Int8	224x224x3	65.48 %
sqnxt23_x200_pt	Float	224x224x3	70.56 %
sqnxt23_x200_pt	Int8	224x224x3	70.25 %
sqnxt23v5_x100_pt	Float	224x224x3	59.85 %
sqnxt23v5_x100_pt	Int8	224x224x3	59.57 %
sqnxt23v5_x150_pt	Float	224x224x3	67.32 %
sqnxt23v5_x150_pt	Int8	224x224x3	66.78 %
sqnxt23v5_x200_pt	Float	224x224x3	71.42 %
sqnxt23v5_x200_pt	Int8	224x224x3	71.02 %

Model	Format	Resolution	Top 1 Accuracy
sqnxt23_x100_pt	Float	224x224x3	58.18 %
sqnxt23_x100_pt	Int8	224x224x3	57.86 %
sqnxt23_x150_pt	Float	224x224x3	66.17 %
sqnxt23_x150_pt	Int8	224x224x3	65.48 %
sqnxt23_x200_pt	Float	224x224x3	70.56 %
sqnxt23_x200_pt	Int8	224x224x3	70.25 %
sqnxt23v5_x100_pt	Float	224x224x3	59.85 %
sqnxt23v5_x100_pt	Int8	224x224x3	59.57 %
sqnxt23v5_x150_pt	Float	224x224x3	67.32 %
sqnxt23v5_x150_pt	Int8	224x224x3	66.78 %
sqnxt23v5_x200_pt	Float	224x224x3	71.42 %
sqnxt23v5_x200_pt	Int8	224x224x3	71.02 %

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: SqueezeNext — https://github.com/amirgholami/SqueezeNext