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--- |
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license: apache-2.0 |
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pipeline_tag: image-classification |
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--- |
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# ShuffleNet V2 |
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## **Use case** : `Image classification` |
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# Model description |
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ShuffleNet V2 is designed following **practical guidelines for efficient CNN architecture design**. It uses channel shuffle operations and a split-concat structure for efficient feature reuse with minimal memory access cost. |
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The architecture features **channel shuffle** operations to enable information flow between channel groups, with a **split-concat architecture** for efficient feature processing. Designed based on **practical guidelines** using direct speed measurement rather than FLOPs, the architecture makes choices that **minimize memory access cost**. |
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ShuffleNet V2 is well-suited for mobile applications with strict efficiency requirements, real-time video processing, and multi-model deployment scenarios. |
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(source: https://arxiv.org/abs/1807.11164) |
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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 | |
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|--------------------|-------| |
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| Framework | Torch | |
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| MParams | ~1.34–2.21 M | |
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| Quantization | Int8 | |
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| Provenance | https://github.com/megvii-model/ShuffleNet-Series | |
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| Paper | https://arxiv.org/abs/1807.11164 | |
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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 | |
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| ----- | ----------- | |
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| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
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| Output Shape | Description | |
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| ----- | ----------- | |
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| (1, P) | Per-class confidence for P classes in FLOAT32| |
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## Recommended platforms |
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| Platform | Supported | Recommended | |
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|----------|-----------|-----------| |
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| STM32L0 |[]|[]| |
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| STM32L4 |[]|[]| |
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| STM32U5 |[]|[]| |
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| STM32H7 |[]|[]| |
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| STM32MP1 |[]|[]| |
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| STM32MP2 |[]|[]| |
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| 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. |
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- 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) |
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| Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |
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|-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| |
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| [shufflenetv2_x050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x050_pt_224/shufflenetv2_x050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 441 | 0 | 1369.07 | 3.0.0 | |
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| [shufflenetv2b_x050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x050_pt_224/shufflenetv2b_x050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 441 | 0 | 1369.07 | 3.0.0 | |
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| [shufflenetv2_x100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x100_pt_224/shufflenetv2_x100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 459.38 | 0 | 2262.45 | 3.0.0 | |
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| [shufflenetv2b_x100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x100_pt_224/shufflenetv2b_x100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 459.38 | 0 | 2263.57 | 3.0.0 | |
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### Reference **NPU** inference time on Imagenet dataset (see Accuracy for details on dataset) |
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| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
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|--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------| |
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| [shufflenetv2_x050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x050_pt_224/shufflenetv2_x050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 8.35 | 119.76 | 3.0.0 | |
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| [shufflenetv2_x100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x100_pt_224/shufflenetv2_x100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 32.43 | 30.84 | 3.0.0 | |
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| [shufflenetv2b_x050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x050_pt_224/shufflenetv2b_x050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 8.39 | 119.19 | 3.0.0 | |
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| [shufflenetv2b_x100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x100_pt_224/shufflenetv2b_x100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 32.65 | 30.63 | 3.0.0 | |
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### Accuracy with Imagenet dataset |
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| Model | Format | Resolution | Top 1 Accuracy | |
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| --- | --- | --- | --- | |
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| [shufflenetv2_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x050_pt_224/shufflenetv2_x050_pt_224.onnx) | Float | 224x224x3 | 60.63 % | |
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| [shufflenetv2_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x050_pt_224/shufflenetv2_x050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.69 % | |
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| [shufflenetv2_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x100_pt_224/shufflenetv2_x100_pt_224.onnx) | Float | 224x224x3 | 69.29 % | |
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| [shufflenetv2_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x100_pt_224/shufflenetv2_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 68.65 % | |
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| [shufflenetv2b_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x050_pt_224/shufflenetv2b_x050_pt_224.onnx) | Float | 224x224x3 | 60.90 % | |
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| [shufflenetv2b_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x050_pt_224/shufflenetv2b_x050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.62 % | |
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| [shufflenetv2b_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x100_pt_224/shufflenetv2b_x100_pt_224.onnx) | Float | 224x224x3 | 70.40 % | |
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| [shufflenetv2b_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x100_pt_224/shufflenetv2b_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 69.59 % | |
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| Model | Format | Resolution | Top 1 Accuracy | |
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| --- | --- | --- | --- | |
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| [shufflenetv2_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x050_pt_224/shufflenetv2_x050_pt_224.onnx) | Float | 224x224x3 | 60.63 % | |
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| [shufflenetv2_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x050_pt_224/shufflenetv2_x050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.69 % | |
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| [shufflenetv2_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x100_pt_224/shufflenetv2_x100_pt_224.onnx) | Float | 224x224x3 | 69.29 % | |
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| [shufflenetv2_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2_x100_pt_224/shufflenetv2_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 68.65 % | |
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| [shufflenetv2b_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x050_pt_224/shufflenetv2b_x050_pt_224.onnx) | Float | 224x224x3 | 60.90 % | |
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| [shufflenetv2b_x050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x050_pt_224/shufflenetv2b_x050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.62 % | |
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| [shufflenetv2b_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x100_pt_224/shufflenetv2b_x100_pt_224.onnx) | Float | 224x224x3 | 70.40 % | |
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| [shufflenetv2b_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/shufflenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/shufflenetv2b_x100_pt_224/shufflenetv2b_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 69.59 % | |
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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**: ShuffleNet V2 — https://github.com/megvii-model/ShuffleNet-Series |
