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STMicroelectronics/
peleenet_pt

image-classification

1804.06882

apache-2.0

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PeleeNet

Use case : `Image classification`

Model description

PeleeNet is a lightweight convolutional neural network architecture designed for efficient real-time inference on mobile and edge devices. It is built upon dense connectivity principles, similar to DenseNet, but optimized to significantly reduce computational cost while maintaining strong accuracy.

Unlike MobileNet, PeleeNet does not rely on width multipliers or depthwise separable convolutions. Instead, it uses two-way dense blocks, bottleneck layers, and efficient stem blocks to reduce memory access cost and improve speed on practical hardware.

The original paper demonstrates that PeleeNet achieves competitive accuracy compared to MobileNet while requiring fewer parameters and lower latency, especially on real devices.

(source: https://arxiv.org/abs/1804.06882)

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

Network information

Network Information	Value
Framework	Torch
MParams	~2.69 M
Quantization	Int8
Provenance	https://github.com/Robert-JunWang/PeleeNet
Paper	https://arxiv.org/abs/1804.06882

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 STM32Edge.AI 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
peleenet_pt_224	Imagenet	Int8	224×224×3	STM32N6	1421	0	2751.06	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
peleenet_pt_224	Imagenet	Int8	224x224x3	STM32N6570-DK	NPU/MCU	18.01	55.52	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
peleenet_pt_224	Int8	224x224x3	70.37%
peleenet_pt_224	Float	224x224x3	70.57%

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: PeleeNet — https://github.com/Robert-JunWang/PeleeNet

STMicroelectronics/peleenet_pt