---
license: other
license_name: sla0044
license_link: >-
https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/LICENSE.md
pipeline_tag: image-classification
---
# 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]|[]|
| STM32N6 |[x]|[]|
# Performances
## 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnist/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnist_v1_28_tfs/st_mnist_v1_28_tfs_int8.tflite) | Int8 | 28x28x1 | STM32H7 | 17.21 KiB | 4.49 KiB | 10.08 KiB | 46.8 KiB | 21.7 KiB | 56.88 KiB | 10.0.0 |
### 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnist/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnist_v1_28_tfs/st_mnist_v1_28_tfs_int8.tflite) | Int8 | 28x28x1 | STM32H747I-DISCO | 400 MHz | 3.41 ms | 10.0.0 |
### 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnist/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnist_v1_28_tfs/st_mnist_v1_28_tfs_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnist/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnist_v1_28_tfs/st_mnist_v1_28_tfs_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnist/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnist_v1_28_tfs/st_mnist_v1_28_tfs_int8.tflite) | 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](https://www.nist.gov/itl/products-and-services/emnist-dataset) , 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnist/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnist_v1_28_tfs/st_mnist_v1_28_tfs.h5) | Float | 28x28x1 | 91.89 % |
| [ST MNIST Byclass v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnist/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnist_v1_28_tfs/st_mnist_v1_28_tfs_int8.tflite) | 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 Integration in a simple example:
Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
# 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