README.md

ST MNIST v1

Use case : Image classification

Model description

This folder contains a custom model ST-MNIST for MNIST type datasets. ST-MNIST model is a depthwise separable convolutional based model architecture and can be used for different MNIST use-cases, e.g. alphabet recognition, digit recognition, or fashion MNIST etc.

ST-MNIST model accepts an input shape of 28 x 28, which is standard for MNIST type datasets. The pretrained model is also quantized in int8 using tensorflow lite converter.

Network information

Network Information	Value
Framework	TensorFlow Lite
Quantization	int8

Network inputs / outputs

For an image resolution of 28x28 and 36 classes : 10 integers (from 0-9) and 26 alphabets (upper-case A-Z)

Input Shape	Description
(1, 28, 28, 1)	Single 28x28 grey-scale image with UINT8 values between 0 and 255

Output Shape	Description
(1, 36)	Per-class confidence for 36 classes in FLOAT32

Recommended Platforms

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

Performances

Training

This model is trained on a subset version of EMNIST dataset. In these experiment we kept only the ten digits classes [0,9] and capital alphabets [A-Z] from the matlab version of the EMNIST byclass dataset. The dataset can be downloaded from the link. We used all the samples for these classes and we have 533,993 images in train dataset, and 89,264 images for the for the test dataset.

The training configurations for the training model are provided in, stmnist_v1_tfs_config.yaml file.

Deployment

To deploy your trained model, you need to configure the user_config.yaml file following the tutorial under the deployment section and deploy the .tflite model.

Metrics

Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.

Reference MCU memory footprint based on EMNIST-Byclass dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Series	Activation RAM	Runtime RAM	Weights Flash	Code Flash	Total RAM	Total Flash	STM32Cube.AI version
ST MNIST Byclass v1 tfs	Int8	28x28x1	STM32H7	17.21 KiB	4.49 KiB	10.08 KiB	46.8 KiB	21.7 KiB	56.88 KiB	9.1.0

Reference MCU inference time based on EMNIST-Byclass dataset (see Accuracy for details on dataset)

Model	Format	Resolution	Board	Frequency	Inference time (ms)	STM32Cube.AI version
ST MNIST Byclass v1 tfs	Int8	28x28x1	STM32H747I-DISCO	400 MHz	3.41 ms	9.1.0

Reference MPU inference time based on EMNIST-Byclass 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 MNIST Byclass v1 tfs	Int8	28x28x1	per-channel**	STM32MP257F-DK2	2 CPU	1500 MHz	0.31 ms	0	0	100	v5.1.0	TensorFlowLite 2.11.0
ST MNIST Byclass v1 tfs	Int8	28x28x1	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	0.69 ms	NA	NA	100	v5.1.0	TensorFlowLite 2.11.0
ST MNIST Byclass v1 tfs	Int8	28x28x1	per-channel	STM32MP135F-DK2	1 CPU	1000 MHz	1.070 ms	NA	NA	100	v5.1.0	TensorFlowLite 2.11.0

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

Accuracy with EMNIST-Byclass dataset

Dataset details: link , by_class, digits from [0-9] and capital letters [A-Z]. Number of classes: 36, Number of train images: 533,993, Number of test images: 89,264.

Model	Format	Resolution	Top 1 Accuracy
ST MNIST Byclass v1 tfs	Float	28x28x1	91.89 %
ST MNIST Byclass v1 tfs	Int8	28x28x1	91.47 %

Following we provide the confusion matrix for the model with Float32 weights.

Following we provide the confusion matrix for the quantized model with INT8 weights.

Retraining and code generation

Please refer to the yaml explanations: here

Demos

Integration in a simple example

Please refer to the generic guideline here

References

[1] "EMNIST : NIST Special Dataset," [Online]. Available: https://www.nist.gov/itl/products-and-services/emnist-dataset.

[2] "EMNIST: an extension of MNIST to handwritten letters". https://arxiv.org/abs/1702.05373