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| license: apache-2.0 |
| pipeline_tag: image-classification |
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| # SqueezeNet |
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| ## **Use case** : `Image classification` |
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| # Model description |
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| SqueezeNet is a pioneering compact architecture that achieves **AlexNet-level accuracy with 50x fewer parameters**. It introduced the "Fire module" combining squeeze and expand operations. |
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| The architecture features **Fire Modules** with squeeze (1x1) followed by expand (1x1 + 3x3) layers, employing **delayed downsampling** to maintain larger activation maps longer. It uses **no fully connected layers**, relying on global average pooling, resulting in an **extremely compact** model (<0.5MB original size). |
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| SqueezeNet is ideal for extremely constrained deployment scenarios, model compression research, and applications where model size is critical. |
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| (source: https://arxiv.org/abs/1602.07360) |
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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 | |
| |--------------------|-------| |
| | Framework | Torch | |
| | MParams | ~1.24 M | |
| | Quantization | Int8 | |
| | Provenance | https://github.com/DeepScale/SqueezeNet | |
| | Paper | https://arxiv.org/abs/1602.07360 | |
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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 | |
| | ----- | ----------- | |
| | (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
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| | Output Shape | Description | |
| | ----- | ----------- | |
| | (1, P) | Per-class confidence for P classes in FLOAT32| |
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| ## Recommended platforms |
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| | Platform | Supported | Recommended | |
| |----------|-----------|-----------| |
| | STM32L0 |[]|[]| |
| | STM32L4 |[]|[]| |
| | STM32U5 |[]|[]| |
| | STM32H7 |[]|[]| |
| | STM32MP1 |[]|[]| |
| | STM32MP2 |[]|[]| |
| | 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. |
| - 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) |
| | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |
| |-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| |
| | [squeezenetv10_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/squeezenetv10_pt_224/squeezenetv10_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2278.12 | 6683.06 | 1266.61 | 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 | |
| |--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------| |
| | [squeezenetv10_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/squeezenetv10_pt_224/squeezenetv10_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 121.20 | 8.25 | 3.0.0 | |
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| ### Accuracy with Imagenet dataset |
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| | Model | Format | Resolution | Top 1 Accuracy | |
| | --- | --- | --- | --- | |
| | [squeezenetv10_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/squeezenetv10_pt_224/squeezenetv10_pt_224.onnx) | Float | 224x224x3 | 62.11 % | |
| | [squeezenetv10_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/squeezenetv10_pt_224/squeezenetv10_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 58.43 % | |
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| | Model | Format | Resolution | Top 1 Accuracy | |
| | --- | --- | --- | --- | |
| | [squeezenetv10_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/squeezenetv10_pt_224/squeezenetv10_pt_224.onnx) | Float | 224x224x3 | 62.11 % | |
| | [squeezenetv10_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenet_pt/Public_pretrainedmodel_public_dataset/Imagenet/squeezenetv10_pt_224/squeezenetv10_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 58.43 % | |
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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**: SqueezeNet — https://github.com/DeepScale/SqueezeNet |
