--- license: apache-2.0 pipeline_tag: image-classification --- # MobileNet V1 ## **Use case** : `Image classification` # Model description The original MobileNet architecture pioneered the use of **depthwise separable convolutions** for efficient mobile vision. It dramatically reduces computation and model size while maintaining competitive accuracy. MobileNet factorizes standard convolutions into **depthwise and pointwise operations**, dramatically reducing computational cost. The architecture supports a **width multiplier (Alpha)** to scale channel dimensions (a025 = 0.25x, a050 = 0.5x, a075 = 0.75x), and uses **linear bottleneck** for efficient channel expansion and compression. Resolution multipliers can further scale input resolution for additional efficiency, making MobileNet ideal for real-time mobile applications and resource-constrained embedded systems. (source: https://arxiv.org/abs/1704.04861) The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment. ## Network information | Network Information | Value | |--------------------|-------| | Framework | Torch | | MParams | ~0.46–2.55 M | | Quantization | Int8 | | Provenance | https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet | | Paper | https://arxiv.org/abs/1704.04861 | ## 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 | |-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| | [mobilenet_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a025_pt_224/mobilenet_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 392 | 0 | 469.45 | 3.0.0 | | [mobilenet_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a050_pt_224/mobilenet_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 588 | 0 | 1318.3 | 3.0.0 | | [mobilenet_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a075_pt_224/mobilenet_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1323 | 0 | 2612.79 | 3.0.0 | | [mobilenetb_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a025_pt_224/mobilenetb_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 392 | 0 | 469.3 | 3.0.0 | | [mobilenetb_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a050_pt_224/mobilenetb_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 588 | 0 | 1317.91 | 3.0.0 | | [mobilenetb_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a075_pt_224/mobilenetb_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1323 | 0 | 2602.29 | 3.0.0 | ### Reference **NPU** inference time and imagenet dataset (see Accuracy for details on dataset) | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |--------|---------|--------|--------|-------------|-----------------|-----------------|-------------------|----------------------| | [mobilenet_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a025_pt_224/mobilenet_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 2.98 | 335.57 | 3.0.0 | | [mobilenet_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a050_pt_224/mobilenet_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 6.55 | 152.67 | 3.0.0 | | [mobilenet_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a075_pt_224/mobilenet_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 11.73 | 85.25 | 3.0.0 | | [mobilenetb_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a025_pt_224/mobilenetb_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 2.89 | 345.90 | 3.0.0 | | [mobilenetb_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a050_pt_224/mobilenetb_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 6.65 | 150.38 | 3.0.0 | | [mobilenetb_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a075_pt_224/mobilenetb_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 11.70 | 85.47 | 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 | | --- | --- | --- | --- | | [mobilenet_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a025_pt_224/mobilenet_a025_pt_224.onnx) | Float | 224x224x3 | 54.81 % | | [mobilenet_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a025_pt_224/mobilenet_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 50.55 % | | [mobilenet_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a050_pt_224/mobilenet_a050_pt_224.onnx) | Float | 224x224x3 | 66.60 % | | [mobilenet_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a050_pt_224/mobilenet_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 64.37 % | | [mobilenet_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a075_pt_224/mobilenet_a075_pt_224.onnx) | Float | 224x224x3 | 71.01 % | | [mobilenet_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenet_a075_pt_224/mobilenet_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 69.91 % | | [mobilenetb_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a025_pt_224/mobilenetb_a025_pt_224.onnx) | Float | 224x224x3 | 55.53 % | | [mobilenetb_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a025_pt_224/mobilenetb_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 53.81 % | | [mobilenetb_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a050_pt_224/mobilenetb_a050_pt_224.onnx) | Float | 224x224x3 | 67.44 % | | [mobilenetb_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a050_pt_224/mobilenetb_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 65.96 % | | [mobilenetb_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a075_pt_224/mobilenetb_a075_pt_224.onnx) | Float | 224x224x3 | 71.46 % | | [mobilenetb_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetb_a075_pt_224/mobilenetb_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 69.72 % | ## Retraining and Integration in a simple example: Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) # References [1] - **Dataset**: Imagenet (ILSVRC 2012) — https://www.image-net.org/ [2] - **Model**: MobileNets — https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md