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STMicroelectronics/
preresnet18_pt

image-classification

1603.05027

apache-2.0

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PreResNet

Use case : `Image classification`

Model description

Pre-activation ResNet (PreResNet) is a variant of ResNet that places batch normalization and activation before convolutions. This simple change improves both training dynamics and final accuracy.

PreResNet employs a pre-activation design with BN-ReLU-Conv order instead of Conv-BN-ReLU, enabling cleaner identity mappings for improved information flow through residual connections. The improved gradient flow during training results from full pre-activation applied to both main path and shortcut connections.

The architecture is well-suited for deep learning research, transfer learning with pre-activation benefits, and applications where training dynamics matter.

(source: https://arxiv.org/abs/1603.05027)

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

Network information

Network Information	Value
Framework	Torch
MParams	~3.75 M
Quantization	Int8
Provenance	https://github.com/KaimingHe/resnet-1k-layers
Paper	https://arxiv.org/abs/1603.05027

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
preresnet18_a025_pt_224	Imagenet	Int8	224×224×3	STM32N6	1323	0	3843.64	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
preresnet18_a025_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	14.35	69.69	3.0.0

Accuracy with Imagenet dataset

Model	Format	Resolution	Top 1 Accuracy
preresnet18_a025_pt	Float	224x224x3	60.99 %
preresnet18_a025_pt	Int8	224x224x3	59.79 %

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
preresnet18_a025_pt	Float	224x224x3	60.99 %
preresnet18_a025_pt	Int8	224x224x3	59.79 %

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: PreResNet — https://github.com/KaimingHe/resnet-1k-layers

STMicroelectronics/preresnet18_pt