--- license: apache-2.0 pipeline_tag: image-classification --- # FDMobileNet ## **Use case** : `Image classification` # Model description Fast-Downsampling MobileNet (FDMobileNet) is an optimized variant of MobileNet designed for **extremely fast inference**. It achieves speed improvements through aggressive early spatial reduction while maintaining reasonable accuracy. FDMobileNet employs a **fast downsampling strategy** that reduces spatial dimensions early in the network to minimize computation. It retains **depthwise separable convolutions** inherited from MobileNet for parameter efficiency, and uses a **width multiplier (Alpha)** to scale the number of channels (a025 = 0.25x, a050 = 0.5x, a075 = 0.75x). Among the fastest models in the model zoo, FDMobileNet is ideal for ultra-low-latency real-time applications and battery-powered devices with strict power constraints. (source: https://arxiv.org/abs/1802.03750) The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment. ## Network information | Network Information | Value | |--------------------|-------| | Framework | Torch | | MParams | ~0.37–1.77 M | | Quantization | Int8 | | Provenance | https://github.com/qinzheng93/FD-MobileNet | | Paper | https://arxiv.org/abs/1802.03750 | ## 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 | |-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| | [fdmobilenet_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a025_pt_224/fdmobilenet_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 294 | 0 | 377.03 | 3.0.0 | | [fdmobilenet_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a050_pt_224/fdmobilenet_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 343 | 0 | 973.39 | 3.0.0 | | [fdmobilenet_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a075_pt_224/fdmobilenet_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 441 | 0 | 1813.66 | 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 | |-------|---------|--------|--------|------------|-------|-----------------|-------------------|---------------------| | [fdmobilenet_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a025_pt_224/fdmobilenet_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 1.88 | 531.91 | 3.0.0 | | [fdmobilenet_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a050_pt_224/fdmobilenet_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 4.07 | 245.70 | 3.0.0 | | [fdmobilenet_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a075_pt_224/fdmobilenet_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 6.83 | 146.41 | 3.0.0 | ### Accuracy with Imagenet dataset | Model | Format | Resolution | Top 1 Accuracy | | --- | --- | --- | --- | | [fdmobilenet_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a025_pt_224/fdmobilenet_a025_pt_224.onnx) | Float | 224x224x3 | 45.37 % | | [fdmobilenet_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a025_pt_224/fdmobilenet_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 29.73 % | | [fdmobilenet_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a050_pt_224/fdmobilenet_a050_pt_224.onnx) | Float | 224x224x3 | 58.04 % | | [fdmobilenet_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a050_pt_224/fdmobilenet_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 41.58 % | | [fdmobilenet_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a075_pt_224/fdmobilenet_a075_pt_224.onnx) | Float | 224x224x3 | 62.10 % | | [fdmobilenet_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a075_pt_224/fdmobilenet_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 60.29 % | | Model | Format | Resolution | Top 1 Accuracy | | --- | --- | --- | --- | | [fdmobilenet_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a025_pt_224/fdmobilenet_a025_pt_224.onnx) | Float | 224x224x3 | 45.37 % | | [fdmobilenet_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a025_pt_224/fdmobilenet_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 29.73 % | | [fdmobilenet_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a050_pt_224/fdmobilenet_a050_pt_224.onnx) | Float | 224x224x3 | 58.04 % | | [fdmobilenet_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a050_pt_224/fdmobilenet_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 41.58 % | | [fdmobilenet_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a075_pt_224/fdmobilenet_a075_pt_224.onnx) | Float | 224x224x3 | 62.10 % | | [fdmobilenet_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/fdmobilenet_a075_pt_224/fdmobilenet_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 60.29 % | ## 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**: FD-MobileNet — https://arxiv.org/abs/1802.03750