| --- |
| license: other |
| license_name: sla0044 |
| license_link: >- |
| https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/st_mnistv1/ST_pretrainedmodel_public_dataset/LICENSE.md |
| pipeline_tag: image-classification |
| --- |
| # ST MNIST v1 |
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| ## **Use case** : `Image classification` |
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| # Model description |
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| 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. |
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| 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. |
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| ## Network information |
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| | Network Information | Value | |
| |-------------------------|-----------------| |
| | Framework | TensorFlow Lite | |
| | Quantization | int8 | |
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| ## Network inputs / outputs |
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| For an image resolution of 28x28 and 36 classes : 10 integers (from 0-9) and 26 alphabets (upper-case A-Z) |
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| | Input Shape | Description | |
| | ----- | ----------- | |
| | (1, 28, 28, 1) | Single 28x28 grey-scale image with UINT8 values between 0 and 255 | |
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| | Output Shape | Description | |
| | ----- | ----------- | |
| | (1, 36) | Per-class confidence for 36 classes in FLOAT32| |
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| ## Recommended Platforms |
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| | Platform | Supported | Recommended | |
| |----------|-----------|-----------| |
| | STM32L0 |[]|[]| |
| | STM32L4 |[x]|[x]| |
| | STM32U5 |[x]|[x]| |
| | STM32H7 |[x]|[x]| |
| | STM32MP1 |[x]|[]| |
| | STM32MP2 |[x]|[]| |
| | STM32N6 |[x]|[]| |
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| # Performances |
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| ## Metrics |
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| - 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. |
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| ### Reference **MCU** memory footprint based on EMNIST-Byclass dataset (see Accuracy for details on dataset) |
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| | Model | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STEdgeAI Core version | |
| |-------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------| |
| | [ST MNIST Byclass v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnistv1/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnistv1_28_tfs/st_mnistv1_28_tfs_int8.tflite) | Int8 | 28x28x1 | STM32H7 | 17.21 KiB | 0.3 KiB | 10.08 KiB | 27.99 KiB | 17.51 KiB | 38.07 KiB | 3.0.0 | |
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| ### Reference **MCU** inference time based on EMNIST-Byclass dataset (see Accuracy for details on dataset) |
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| | Model | Format | Resolution | Board | Frequency | Inference time (ms) | STEdgeAI Core version | |
| |-------------------|--------|------------|------------------|---------------|---------------------|-----------------------| |
| | [ST MNIST Byclass v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnistv1/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnistv1_28_tfs/st_mnistv1_28_tfs_int8.tflite) | Int8 | 28x28x1 | STM32H747I-DISCO | 400 MHz | 3.48 ms | 3.0.0 | |
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| ### Reference **MPU** inference time based on EMNIST-Byclass dataset (see Accuracy for details on dataset) |
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| | 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_mnistv1/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnistv1_28_tfs/st_mnistv1_28_tfs_int8.tflite) | Int8 | 28x28x1 | per-channel** | STM32MP257F-DK2 | 2 CPU | 1500 MHz | 0.87 | 64.23 | 35.77 | 0 | v6.1.0 | TensorFlowLite 2.18.0 | |
| | [ST MNIST Byclass v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnistv1/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnistv1_28_tfs/st_mnistv1_28_tfs_int8.tflite) | Int8 | 28x28x1 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 0.70 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
| | [ST MNIST Byclass v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnistv1/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnistv1_28_tfs/st_mnistv1_28_tfs_int8.tflite) | Int8 | 28x28x1 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 1.02 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
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| ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization** |
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| ** **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. |
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| ### Accuracy with EMNIST-Byclass dataset |
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| 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. |
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| | Model | Format | Resolution | Top 1 Accuracy | |
| |-------|--------|------------|----------------| |
| | [ST MNIST Byclass v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnistv1/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnistv1_28_tfs/st_mnistv1_28_tfs.keras) | Float | 28x28x1 | 91.89 % | |
| | [ST MNIST Byclass v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_mnistv1/ST_pretrainedmodel_public_dataset/emnist_byclass/st_mnistv1_28_tfs/st_mnistv1_28_tfs_int8.tflite) | Int8 | 28x28x1 | 91.47 % | |
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| Following we provide the confusion matrix for the model with Float32 weights. |
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| Following we provide the confusion matrix for the quantized model with INT8 weights. |
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| ## Retraining and Integration in a simple example: |
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| Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) |
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| # References |
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| <a id="1">[1]</a> |
| "EMNIST : NIST Special Dataset," [Online]. Available: https://www.nist.gov/itl/products-and-services/emnist-dataset. |
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| <a id="2">[2]</a> |
| "EMNIST: an extension of MNIST to handwritten letters". https://arxiv.org/abs/1702.05373 |
