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 | 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 | 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 | 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 Integration in a simple example:
Please refer to the stm32ai-modelzoo-services GitHub 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