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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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# SqueezeNext |
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## **Use case** : `Image classification` |
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# Model description |
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SqueezeNext is the successor to SqueezeNet, offering **improved accuracy through skip connections, bottleneck modules, and separable convolutions**. It is specifically designed for hardware efficiency. |
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The architecture employs a **two-stage bottleneck** with 1x1 squeeze followed by 1x1-3x3 expand patterns, with **skip connections** added for improved gradient flow. **Separable convolutions** further reduce computational cost, and the **hardware-aware design** is optimized for specific hardware platforms. |
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SqueezeNext is ideal for applications requiring SqueezeNet-style compactness with better accuracy, and hardware platforms with specific optimization targets. |
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(source: https://arxiv.org/abs/1803.10615) |
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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 | ~0.68–3.17 M | |
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| Quantization | Int8 | |
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| Provenance | https://github.com/amirgholami/SqueezeNext | |
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| Paper | https://arxiv.org/abs/1803.10615 | |
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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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| [sqnxt23_x100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x100_pt_224/sqnxt23_x100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2086.45 | 3025 | 693.67 | 3.0.0 | |
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| [sqnxt23_x150_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x150_pt_224/sqnxt23_x150_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2087.48 | 6806.25 | 1453.99 | 3.0.0 | |
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| [sqnxt23_x200_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x200_pt_224/sqnxt23_x200_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2275.52 | 9075 | 2493.33 | 3.0.0 | |
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| [sqnxt23v5_x150_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x150_pt_224/sqnxt23v5_x150_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2087.48 | 6806.25 | 1879.24 | 3.0.0 | |
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| [sqnxt23v5_x200_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x200_pt_224/sqnxt23v5_x200_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2275.52 | 9075 | 3249.45 | 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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| [sqnxt23_x100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x100_pt_224/sqnxt23_x100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 87.07 | 11.49 | 3.0.0 | |
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| [sqnxt23_x150_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x150_pt_224/sqnxt23_x150_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 127.46 | 7.85 | 3.0.0 | |
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| [sqnxt23_x200_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x200_pt_224/sqnxt23_x200_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 182.12 | 5.49 | 3.0.0 | |
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| [sqnxt23v5_x100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x100_pt_224/sqnxt23v5_x100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 86.37 | 11.58 | 3.0.0 | |
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| [sqnxt23v5_x150_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x150_pt_224/sqnxt23v5_x150_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 126.91 | 7.88 | 3.0.0 | |
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| [sqnxt23v5_x200_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x200_pt_224/sqnxt23v5_x200_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 181.01 | 5.52 | 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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| [sqnxt23_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x100_pt_224/sqnxt23_x100_pt_224.onnx) | Float | 224x224x3 | 58.18 % | |
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| [sqnxt23_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x100_pt_224/sqnxt23_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 57.86 % | |
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| [sqnxt23_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x150_pt_224/sqnxt23_x150_pt_224.onnx) | Float | 224x224x3 | 66.17 % | |
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| [sqnxt23_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x150_pt_224/sqnxt23_x150_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 65.48 % | |
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| [sqnxt23_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x200_pt_224/sqnxt23_x200_pt_224.onnx) | Float | 224x224x3 | 70.56 % | |
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| [sqnxt23_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x200_pt_224/sqnxt23_x200_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 70.25 % | |
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| [sqnxt23v5_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x100_pt_224/sqnxt23v5_x100_pt_224.onnx) | Float | 224x224x3 | 59.85 % | |
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| [sqnxt23v5_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x100_pt_224/sqnxt23v5_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.57 % | |
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| [sqnxt23v5_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x150_pt_224/sqnxt23v5_x150_pt_224.onnx) | Float | 224x224x3 | 67.32 % | |
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| [sqnxt23v5_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x150_pt_224/sqnxt23v5_x150_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 66.78 % | |
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| [sqnxt23v5_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x200_pt_224/sqnxt23v5_x200_pt_224.onnx) | Float | 224x224x3 | 71.42 % | |
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| [sqnxt23v5_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x200_pt_224/sqnxt23v5_x200_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 71.02 % | |
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| Model | Format | Resolution | Top 1 Accuracy | |
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| [sqnxt23_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x100_pt_224/sqnxt23_x100_pt_224.onnx) | Float | 224x224x3 | 58.18 % | |
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| [sqnxt23_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x100_pt_224/sqnxt23_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 57.86 % | |
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| [sqnxt23_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x150_pt_224/sqnxt23_x150_pt_224.onnx) | Float | 224x224x3 | 66.17 % | |
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| [sqnxt23_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x150_pt_224/sqnxt23_x150_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 65.48 % | |
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| [sqnxt23_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x200_pt_224/sqnxt23_x200_pt_224.onnx) | Float | 224x224x3 | 70.56 % | |
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| [sqnxt23_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23_x200_pt_224/sqnxt23_x200_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 70.25 % | |
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| [sqnxt23v5_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x100_pt_224/sqnxt23v5_x100_pt_224.onnx) | Float | 224x224x3 | 59.85 % | |
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| [sqnxt23v5_x100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x100_pt_224/sqnxt23v5_x100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 59.57 % | |
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| [sqnxt23v5_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x150_pt_224/sqnxt23v5_x150_pt_224.onnx) | Float | 224x224x3 | 67.32 % | |
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| [sqnxt23v5_x150_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x150_pt_224/sqnxt23v5_x150_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 66.78 % | |
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| [sqnxt23v5_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x200_pt_224/sqnxt23v5_x200_pt_224.onnx) | Float | 224x224x3 | 71.42 % | |
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| [sqnxt23v5_x200_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/sqnxt_pt/Public_pretrainedmodel_public_dataset/Imagenet/sqnxt23v5_x200_pt_224/sqnxt23v5_x200_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 71.02 % | |
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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**: SqueezeNext — https://github.com/amirgholami/SqueezeNext |
