| --- |
| license: apache-2.0 |
| pipeline_tag: image-classification |
| --- |
| # MobileNet V4 |
|
|
| ## **Use case** : `Image classification` |
|
|
| # Model description |
|
|
|
|
| MobileNetV4 represents the latest evolution in the MobileNet family, introducing **Universal Inverted Bottleneck (UIB) blocks** that unify various efficient convolution designs. It achieves state-of-the-art accuracy-efficiency trade-offs on mobile hardware. |
|
|
| The architecture features a **flexible UIB block design** accommodating various operations, optimized through **Neural Architecture Search** for multiple hardware platforms. It includes **Mobile MQA Attention** as an efficient attention mechanism for mobile deployment, providing enhanced feature extraction with improved capacity per FLOP. |
|
|
| MobileNetV4 is ideal for state-of-the-art mobile vision applications requiring the latest architectural improvements, though it shows quantization sensitivity (~10% drop) that should be considered for INT8 deployment. |
|
|
| (source: https://arxiv.org/abs/2404.10518) |
|
|
| The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment. |
|
|
| ## Network information |
|
|
|
|
| | Network Information | Value | |
| |--------------------|-------| |
| | Framework | Torch | |
| | MParams | ~3.67 M | |
| | Quantization | Int8 | |
| | Provenance | https://github.com/huggingface/pytorch-image-models | |
| | Paper | https://arxiv.org/abs/2404.10518 | |
|
|
| ## Network inputs / outputs |
|
|
|
|
| For an image resolution of NxM and P classes |
|
|
| | Input Shape | Description | |
| | ----- | ----------- | |
| | (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
|
|
| | Output Shape | Description | |
| | ----- | ----------- | |
| | (1, P) | Per-class confidence for P classes in FLOAT32| |
|
|
|
|
| ## Recommended platforms |
|
|
|
|
| | Platform | Supported | Recommended | |
| |----------|-----------|-----------| |
| | STM32L0 |[]|[]| |
| | STM32L4 |[]|[]| |
| | STM32U5 |[]|[]| |
| | STM32H7 |[]|[]| |
| | STM32MP1 |[]|[]| |
| | STM32MP2 |[]|[]| |
| | STM32N6 |[x]|[x]| |
|
|
| # Performances |
|
|
| ## Metrics |
|
|
| - Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option. |
| - All the models are trained from scratch on imagenet dataset |
|
|
| ### 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 | |
| |-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| |
| | [mobilenetv4small_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv4_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv4small_pt_224/mobilenetv4small_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 539 | 0 | 3760.53 | 3.0.0 | |
|
|
|
|
|
|
| ### 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 | |
| |--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------| |
| | [mobilenetv4small_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv4_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv4small_pt_224/mobilenetv4small_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 13.74 | 72.78 | 3.0.0 | |
|
|
|
|
|
|
|
|
| ### Accuracy with Imagenet dataset |
|
|
| 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. |
|
|
| | Model | Format | Resolution | Top 1 Accuracy | |
| | --- | --- | --- | --- | |
| | [mobilenetv4small_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv4_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv4small_pt_224/mobilenetv4small_pt_224.onnx) | Float | 224x224x3 | 74.33 % | |
| | [mobilenetv4small_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv4_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv4small_pt_224/mobilenetv4small_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 64.24 % | |
|
|
|
|
|
|
| ## 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> - **Dataset**: Imagenet (ILSVRC 2012) — https://www.image-net.org/ |
|
|
| <a id="2">[2]</a> - **Model**: MobileNetV4 — https://arxiv.org/abs/2404.10518 |
