| | --- |
| | license: apache-2.0 |
| | pipeline_tag: image-classification |
| | --- |
| | # MnasNet |
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| | ## **Use case** : `Image classification` |
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| | # Model description |
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| | Mobile Neural Architecture Search Network (MnasNet) is designed using **automated neural architecture search (NAS)** specifically targeting mobile devices. It optimizes for both accuracy and real-device latency simultaneously. |
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| | MnasNet employs **multi-objective optimization** to balance accuracy with latency on target devices, using **inverted residual blocks** similar to MobileNetV2 but with NAS-optimized configurations. The **factorized hierarchical search space** enables diverse and efficient architectures. |
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| | The architecture is well-suited for mobile and embedded vision applications, particularly in scenarios requiring optimized accuracy-latency trade-offs. |
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| | (source: https://arxiv.org/abs/1807.11626) |
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| | The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment. |
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| | ## Network information |
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| | | Network Information | Value | |
| | |--------------------|-------| |
| | | Framework | Torch | |
| | | MParams | ~2.27 M | |
| | | Quantization | Int8 | |
| | | Provenance | https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet | |
| | | Paper | https://arxiv.org/abs/1807.11626 | |
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| | ## Network inputs / outputs |
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| | For an image resolution of NxM and P classes |
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| | | Input Shape | Description | |
| | | ----- | ----------- | |
| | | (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
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| | | Output Shape | Description | |
| | | ----- | ----------- | |
| | | (1, P) | Per-class confidence for P classes in FLOAT32| |
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| | ## Recommended platforms |
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| | | Platform | Supported | Recommended | |
| | |----------|-----------|-----------| |
| | | STM32L0 |[]|[]| |
| | | STM32L4 |[]|[]| |
| | | STM32U5 |[]|[]| |
| | | STM32H7 |[]|[]| |
| | | STM32MP1 |[]|[]| |
| | | STM32MP2 |[]|[]| |
| | | STM32N6 |[x]|[x]| |
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| | # Performances |
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| | ## Metrics |
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| | - Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option. |
| | - All the models are trained from scratch on Imagenet dataset |
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| | ### Reference **NPU** memory footprint on Imagenet dataset (see Accuracy for details on dataset) |
| | | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |
| | |-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| |
| | | [mnasnet_d050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mnasnet_d050_pt_224/mnasnet_d050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 612.5 | 0 | 2319.53 | 3.0.0 | |
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| | ### Reference **NPU** inference time on Imagenet dataset (see Accuracy for details on dataset) |
| | | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| | |--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------| |
| | | [mnasnet_d050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mnasnet_d050_pt_224/mnasnet_d050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 11.21 | 89.21 | 3.0.0 | |
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| | ### Accuracy with Imagenet dataset |
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| | | Model | Format | Resolution | Top 1 Accuracy | |
| | | --- | --- | --- | --- | |
| | | [mnasnet_d050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mnasnet_d050_pt_224/mnasnet_d050_pt_224.onnx) | Float | 224x224x3 | 67.50 % | |
| | | [mnasnet_d050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mnasnet_d050_pt_224/mnasnet_d050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.99 % | |
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| | Dataset details: [link](https://www.image-net.org) |
| | Number of classes: 1000. |
| | To perform the quantization, we calibrated the activations with a random subset of the training set. |
| | For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set. |
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| | | Model | Format | Resolution | Top 1 Accuracy | |
| | | --- | --- | --- | --- | |
| | | [mnasnet_d050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mnasnet_d050_pt_224/mnasnet_d050_pt_224.onnx) | Float | 224x224x3 | 67.50 % | |
| | | [mnasnet_d050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mnasnet_d050_pt_224/mnasnet_d050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.99 % | |
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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> - **Dataset**: Imagenet (ILSVRC 2012) — https://www.image-net.org/ |
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| | <a id="2">[2]</a> - **Model**: MnasNet — https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet |
