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README.md

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----
-license: other
-license_name: sla0044
-license_link: >-
-  https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/LICENSE.md
----
+---
+license: other
+license_name: sla0044
+license_link: >-
+  https://github.com/STMicroelectronics/stm32ai-modelzoo/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/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/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/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/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/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/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/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.
+
+![plot](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/st_mnist/doc/img/st_emnist_by_class_confusion_matrix.png)
+
+Following we provide the confusion matrix for the quantized model with INT8 weights.
+
+![plot](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/st_mnist/doc/img/st_emnist_by_class_confusion_matrix_int8.png)
+
+
+## Retraining and Integration in a simple example:
+
+Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
+
+
+# References
+
+
+<a id="1">[1]</a>
+"EMNIST : NIST Special Dataset," [Online]. Available: https://www.nist.gov/itl/products-and-services/emnist-dataset.
+
+<a id="2">[2]</a>
+"EMNIST: an extension of MNIST to handwritten letters". https://arxiv.org/abs/1702.05373
+