ResNet v1

April 16, 2026 · View on GitHub

Use case : `Image classification`

Model description

ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ResNet models consist of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. The models below have 8 and 32 layers with ResNet v1 architecture. (source: https://keras.io/api/applications/resnet/) The model is quantized in int8 using tensorflow lite converter.

Network information

Network Information	Value
Framework	TensorFlow Lite
Quantization	int8
Provenance	https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet
Paper	https://arxiv.org/abs/1512.03385

The models are quantized using tensorflow lite converter.

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	Optimized
STM32L0	[]	[]
STM32L4	[x]	[]
STM32U5	[x]	[]
STM32H7	[x]	[x]
STM32MP1	[x]	[x]*
STM32MP2	[x]	[]
STM32N6	[x]	[]

Only for Cifar 100 models

Performances

Metrics

Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
tfs stands for "training from scratch", meaning that the model weights were randomly initialized before training.
tl stands for "transfer learning", meaning that the model backbone weights were initialized from a pre-trained model, then only the last layer was unfrozen during the training.
fft stands for "full fine-tuning", meaning that the full model weights were initialized from a transfer learning pre-trained model, and all the layers were unfrozen during the training.

Reference MCU memory footprint based on Cifar 10 dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Series	Activation RAM	Runtime RAM	Weights Flash	Code Flash	Total RAM	Total Flash	STEdgeAI Core version
ResNet v1 8 tfs	Int8	32x32x3	STM32H7	62.51 KiB	1.26 KiB	76.9 KiB	36.08 KiB	63.77 KiB	112.98 KiB	4.0.0

Reference MCU inference time based on Cifar 10 dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Board	Execution Engine	Frequency	Inference time (ms)	STEdgeAI Core version
ResNet v1 8 tfs	Int8	32x32x3	STM32H747I-DISCO	1 CPU	400 MHz	28.5 ms	4.0.0

Reference MPU inference time based on Flowers dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Quantization	Board	Execution Engine	Frequency	Inference time (ms)	%NPU	%GPU	%CPU	X-LINUX-AI version	Framework
ResNet v1 8 tfs	Int8	32x32x3	per-channel**	STM32MP257F-DK2	NPU/GPU	800 MHz	2.06	21.76	78.24	0	v6.1.0	OpenVX
ResNet v1 8 tfs	Int8	32x32x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	6.71	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ResNet v1 8 tfs	Int8	32x32x3	per-channel	STM32MP135F-DK2	1 CPU	1000 MHz	10.34	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0

** To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization

** Note: On STM32MP2 devices, per-channel quantized models are internally converted to per-tensor quantization by the compiler using an entropy-based method. This may introduce a slight loss in accuracy compared to the original per-channel models.

Reference MCU memory footprint based on Cifar 100 dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Series	Activation RAM	Runtime RAM	Weights Flash	Code Flash	Total RAM	Total Flash	STEdgeAI Core version
ResNet v1 32 tfs	Int8	32x32x3	STM32H7	45.41 KiB	24.98 KiB	464.38 KiB	78.65 KiB	70.39 KiB	543.03 KiB	4.0.0

Reference MCU inference time based on Cifar 100 dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Board	Execution Engine	Frequency	Inference time (ms)	STEdgeAI Core version
ResNet v1 32 tfs	Int8	32x32x3	STM32H747I-DISCO	1 CPU	400 MHz	177.7 ms	4.0.0

Reference MPU inference time based on Flowers dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Quantization	Board	Execution Engine	Frequency	Inference time (ms)	%NPU	%GPU	%CPU	X-LINUX-AI version	Framework
ResNet v1 32 tfs	Int8	32x32x3	per-channel	STM32MP257F-DK2	NPU/GPU	800 MHz	9.160 ms	14.75	85.25	0	v6.1.0	OpenVX
ResNet v1 32 tfs	Int8	32x32x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	34.78 ms	NA	NA	100	v6.1.0	TensorFlowLite 2.11.0
ResNet v1 32 tfs	Int8	32x32x3	per-channel	STM32MP135F-DK2	1 CPU	1000 MHz	55.32 ms	NA	NA	100	v6.1.0	TensorFlowLite 2.11.0

Accuracy with Cifar10 dataset

Dataset details: link , License CC BY 4.0 , Quotation[1] , Number of classes: 10, Number of images: 60 000

Model	Format	Resolution	Top 1 Accuracy
ResNet v1 8 tfs	Float	32x32x3	87.01 %
ResNet v1 8 tfs	Int8	32x32x3	85.59 %

Accuracy with Cifar100 dataset

Dataset details: link , License CC0 4.0, Quotation[2] , Number of classes:100, Number of images: 600 000

Model	Format	Resolution	Top 1 Accuracy
ResNet v1 32 tfs	Float	32x32x3	67.75 %
ResNet v1 32 tfs	Int8	32x32x3	66.58 %

Retraining and Integration in a simple example:

Please refer to the stm32ai-modelzoo-services GitHub here

References

[1] "Tf_flowers : tensorflow datasets," TensorFlow. [Online]. Available: https://www.tensorflow.org/datasets/catalog/tf_flowers.

[2] J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), "Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network", Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1

[3] L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014.