EfficientNet

April 16, 2026 · View on GitHub

Use case : `Image classification`

Model description

EfficientNet was initially introduced in this paper. The authors proposed a method that uniformly scales all dimensions depth/width/resolution using a so-called compound coefficient. Using neural architecture search, the authors created the EfficientNet topology and starting from B0, derived a few variants B1...B7 ordered by increasing complexity. Its main building blocks are a mobile inverted bottleneck MBConv (Sandler et al., 2018; Tan et al., 2019) and a squeeze-and-excitation optimization (Hu et al., 2018).

EfficientNet provides state-of-the art accuracy on imagenet and CIFAR for example while being much smaller and faster than its comparable (ResNet, DenseNet, Inception...). However, for STM32 platforms, B0 is already too large. That's why, we internally derived a custom version tailored for STM32 and modified it to be quantization-friendly (not discussed in the initial paper). This custom model is then quantized in int8 using Tensorflow Lite converter. In the following, the resulting model is called ST EfficientNet LC v1 (LC standing for Low Complexity).

ST EfficientNet LC v1 was obtained after fine-tuning of the original topology. Our goal was to reach around 500 kBytes for RAM and weights. For achieving this, we decided to replace original 'swish' by a simple 'relu6', and search for good expansion factor, depth and width coefficients. Of course, many models could meet the requirement. We selected the one which was better performing on food101 dataset. We made several attempts to quantize the EfficientNet topology, and discover some issues when quantizing activations. The problem was fixed mainly by adding a clipping lambda layer before the sigmoid.

Network information

Network Information	Value
Framework	TensorFlow Lite
Params	517540
Quantization	int8
Paper	https://arxiv.org/pdf/1905.11946.pdf

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

Recommended platform

Platform	Supported	Recommended
STM32L0	[]	[]
STM32L4	[]	[]
STM32U5	[x]	[]
STM32H7	[x]	[x]
STM32MP1	[x]	[x]
STM32MP2	[x]	[]
STM32N6	[x]	[]

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.

Reference NPU memory footprint on food101 dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Series	Internal RAM (KiB)	Weights Flash (KiB)	STEdgeAI Core version
ST EfficientNet LC v1 tfs	Int8	128x128x3	STM32N6	288	559.59	4.0.0
ST EfficientNet LC v1 tfs	Int8	224x224x3	STM32N6	833.05	550.39	4.0.0
ST EfficientNet LC v1 tfs	Int8/Int4	224x224x3	STM32N6	833.05	481.49	4.0.0

Reference NPU inference time on food101 dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Board	Execution Engine	Inference time (ms)	Inf / sec	STEdgeAI Core version
ST EfficientNet LC v1 tfs	Int8	128x128x3	STM32N6570-DK	NPU/MCU	3.58	279.3	4.0.0
ST EfficientNet LC v1 tfs	Int8	224x224x3	STM32N6570-DK	NPU/MCU	7.04	142.0	4.0.0
ST EfficientNet LC v1 tfs	Int8/Int4	224x224x3	STM32N6570-DK	NPU/MCU	6.96	143.6	4.0.0

Reference MCU memory footprints based on Flowers 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
ST EfficientNet LC v1 tfs	Int8	224x224x3	STM32H7	466.01 KiB	15.6 KiB	505.29 KiB	100.99 KiB	481.61 KiB	606.28 KiB	4.0.0
ST EfficientNet LC v1 tfs	Int8	128x128x3	STM32H7	181.01 KiB	15.6 KiB	505.29 KiB	100.62 KiB	196.61 KiB	605.91 KiB	4.0.0

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

Model	Format	Resolution	Board	Execution Engine	Frequency	Inference time (ms)	STEdgeAI Core version
ST EfficientNet LC v1 tfs	Int8	224x224x3	STM32H747I-DISCO	1 CPU	400 MHz	459.99 ms	4.0.0
ST EfficientNet LC v1 tfs	Int8	128x128x3	STM32H747I-DISCO	1 CPU	400 MHz	155.22 ms	4.0.0
ST EfficientNet LC v1 tfs	Int8	224x224x3	STM32F769I-DISCO	1 CPU	216 MHz	871.7 ms	4.0.0
ST EfficientNet LC v1 tfs	Int8	128x128x3	STM32F769I-DISCO	1 CPU	216 MHz	259.5 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
ST EfficientNet LC v1 tfs	Int8	224x224x3	per-channel**	STM32MP257F-DK2	NPU/GPU	800 MHz	36.82 ms	14.72	85.28	0	v6.1.0	OpenVX
ST EfficientNet LC v1 tfs	Int8	128x128x3	per-channel**	STM32MP257F-DK2	NPU/GPU	800 MHz	14.81 ms	29.68	70.32	0	v6.1.0	OpenVX
ST EfficientNet LC v1 tfs	Int8	224x224x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	137.34 ms	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST EfficientNet LC v1 tfs	Int8	128x128x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	45.80 ms	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST EfficientNet LC v1 tfs	Int8	224x224x3	per-channel	STM32MP135F-DK2	1 CPU	1000 MHz	195.25 ms	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST EfficientNet LC v1 tfs	Int8	128x128x3	per-channel	STM32MP135F-DK2	1 CPU	1000 MHz	65.14 ms	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

Accuracy with Flowers dataset

Dataset details: http://download.tensorflow.org/example_images/flower_photos.tgz , License CC - BY 2.0 Number of classes: 5, 3670 files

Model	Format	Resolution	Top 1 Accuracy (%)
ST EfficientNet LC v1 tfs	Float	224x224x3	90.19
ST EfficientNet LC v1 tfs	Int8	224x224x3	89.92
ST EfficientNet LC v1 tfs	Float	128x128x3	87.19
ST EfficientNet LC v1 tfs	Int8	128x128x3	86.78

Accuracy with Plant dataset

Dataset details: https://data.mendeley.com/datasets/tywbtsjrjv/1 , License CC0 1.0 Number of classes: 39, number of files: 55448

Model	Format	Resolution	Top 1 Accuracy (%)
ST EfficientNet LC v1 tfs	Float	224x224x3	99.86
ST EfficientNet LC v1 tfs	Int8	224x224x3	99.78
ST EfficientNet LC v1 tfs	Float	128x128x3	99.76
ST EfficientNet LC v1 tfs	Int8	128x128x3	99.63

Accuracy with Food-101 dataset

Dataset details: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/, Number of classes: 101, number of files: 101000

Model	Format	Resolution	Top 1 Accuracy (%)
ST EfficientNet LC v1 tfs	Float	224x224x3	74.59
ST EfficientNet LC v1 tfs	Int8	224x224x3	74.02
ST EfficientNet LC v1 tfs	Float	128x128x3	64.11
ST EfficientNet LC v1 tfs	Int8	128x128x3	63.21
ST EfficientNet LC v1 tfs	Int8/Int4	224x224x3	73.12

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.