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license: apache-2.0 |
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pipeline_tag: image-classification |
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--- |
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# RegNet |
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## **Use case** : `Image classification` |
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# Model description |
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RegNet introduces a **design space paradigm** for neural networks. Rather than designing individual architectures, RegNet defines a design space of possible networks characterized by a few parameters, enabling systematic exploration of network designs. |
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The architecture uses **quantized linear parameterization** where networks are defined by simple equations, with systematic variation of **width and depth patterns**. RegNet employs **group convolutions** for efficiency, following a **design space exploration** methodology for finding optimal configurations. |
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RegNet is well-suited for research on neural network design principles, applications requiring systematic architecture selection, and scalable deployments with consistent design principles. |
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(source: https://arxiv.org/abs/2003.13678) |
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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 | ~2.55 M | |
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| Quantization | Int8 | |
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| Provenance | https://github.com/facebookresearch/pycls | |
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| Paper | https://arxiv.org/abs/2003.13678 | |
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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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| [regnetx002_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/regnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/regnetx002_pt_224/regnetx002_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1192.84 | 0 | 2606.72 | 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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| [regnetx002_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/regnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/regnetx002_pt_224/regnetx002_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 9.96 | 100.40 | 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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| [regnetx002_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/regnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/regnetx002_pt_224/regnetx002_pt_224.onnx) | Float | 224x224x3 | 70.72 % | |
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| [regnetx002_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/regnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/regnetx002_pt_224/regnetx002_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 68.95 % | |
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Dataset details: [link](https://www.image-net.org) |
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Number of classes: 1000. |
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To perform the quantization, we calibrated the activations with a random subset of the training set. |
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For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set. |
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| Model | Format | Resolution | Top 1 Accuracy | |
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| --- | --- | --- | --- | |
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| [regnetx002_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/regnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/regnetx002_pt_224/regnetx002_pt_224.onnx) | Float | 224x224x3 | 70.72 % | |
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| [regnetx002_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/regnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/regnetx002_pt_224/regnetx002_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 68.95 % | |
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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**: RegNet — https://github.com/facebookresearch/pycls |
