| | --- |
| | license: apache-2.0 |
| | pipeline_tag: image-classification |
| | --- |
| | # ProxylessNAS |
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| | ## **Use case** : `Image classification` |
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| | # Model description |
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| | ProxylessNAS enables **direct neural architecture search on target hardware**, eliminating the "proxy" task typically used in NAS. It learns specialized architectures optimized for specific devices without costly re-training. |
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| | The architecture employs **direct hardware targeting** by searching directly on target hardware metrics, using **path-level binarization** as an efficient search method with binary architecture parameters. **Latency regularization** incorporates actual latency into the search objective, resulting in **hardware-specific architectures** optimized for different hardware platforms. |
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| | ProxylessNAS achieves high accuracy (74.25% Top-1) with good quantization stability (0.60% drop), making it ideal for applications requiring hardware-optimized architectures with strict latency requirements. |
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| | (source: https://arxiv.org/abs/1812.00332) |
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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 | ~4.13 M | |
| | | Quantization | Int8 | |
| | | Provenance | https://github.com/mit-han-lab/proxylessnas | |
| | | Paper | https://arxiv.org/abs/1812.00332 | |
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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 | |
| | |-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| |
| | | [proxylessnas_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/proxylessnas_pt/Public_pretrainedmodel_public_dataset/Imagenet/proxylessnas_pt_224/proxylessnas_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1372 | 0 | 4233.20 | 3.0.0 | |
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| | ### Reference **NPU** inference time on food101 and imagenet dataset (see Accuracy for details on dataset) |
| | | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| | |--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------| |
| | | [proxylessnas_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/proxylessnas_pt/Public_pretrainedmodel_public_dataset/Imagenet/proxylessnas_pt_224/proxylessnas_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 27.65 | 36.17 | 3.0.0 | |
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| | ### Accuracy with Imagenet dataset |
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| | | Model | Format | Resolution | Top 1 Accuracy | |
| | | --- | --- | --- | --- | |
| | | [proxylessnas_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/proxylessnas_pt/Public_pretrainedmodel_public_dataset/Imagenet/proxylessnas_pt_224/proxylessnas_pt_224.onnx) | Float | 224x224x3 | 74.85 % | |
| | | [proxylessnas_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/proxylessnas_pt/Public_pretrainedmodel_public_dataset/Imagenet/proxylessnas_pt_224/proxylessnas_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 74.25 % | |
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| | | Model | Format | Resolution | Top 1 Accuracy | |
| | | --- | --- | --- | --- | |
| | | [proxylessnas_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/proxylessnas_pt/Public_pretrainedmodel_public_dataset/Imagenet/proxylessnas_pt_224/proxylessnas_pt_224.onnx) | Float | 224x224x3 | 74.85 % | |
| | | [proxylessnas_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/proxylessnas_pt/Public_pretrainedmodel_public_dataset/Imagenet/proxylessnas_pt_224/proxylessnas_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 74.25 % | |
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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**: ProxylessNAS — https://github.com/MIT-HAN-LAB/ProxylessNAS |
