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--- |
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license: other |
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license_name: sla0044 |
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license_link: >- |
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https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/LICENSE.md |
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pipeline_tag: image-classification |
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--- |
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# ST ResNet |
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## **Use case** : `Image classification` |
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# Model description |
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ST ResNet is STMicroelectronics' custom ResNet family **specifically designed and optimized for STM32 deployment**. It offers a range of sizes from "pico" to "tiny" with ReLU activations, providing a progressive accuracy-efficiency trade-off tailored for embedded vision applications. |
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The architecture is **STM32-optimized** and designed specifically for STM32 NPU deployment, with **progressive sizing** from Pico → Nano → Micro → Milli → Tiny (increasing capacity). It uses **ReLU activation** for quantization friendliness and a **balanced design** optimized for both accuracy and inference speed on target hardware. |
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ST ResNet models are the **recommended choice** for production STM32 deployments, with all variants running on internal RAM only and well-characterized performance on STM32 hardware. |
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(source: https://arxiv.org/abs/2601.05364, https://arxiv.org/abs/2511.11716) |
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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 | ~0.59–3.97 M | |
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| Quantization | Int8 | |
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| Provenance | https://github.com/STMicroelectronics/stm32ai-modelzoo | |
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| Paper | https://arxiv.org/abs/2601.05364 | |
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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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| [st_resnetpico_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetpico_actrelu_pt_224/st_resnetpico_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 784 | 0 | 607.27 | 3.0.0 | |
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| [st_resnetnano_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetnano_actrelu_pt_224/st_resnetnano_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 759.5 | 0 | 992.04 | 3.0.0 | |
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| [st_resnetmicro_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmicro_actrelu_pt_224/st_resnetmicro_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 882 | 0 | 1534.12 | 3.0.0 | |
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| [st_resnetmilli_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmilli_actrelu_pt_224/st_resnetmilli_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1421 | 0 | 3059.81 | 3.0.0 | |
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| [st_resnettiny_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnettiny_actrelu_pt_224/st_resnettiny_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2205 | 0 | 4060.57 | 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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| [st_resnetpico_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetpico_actrelu_pt_224/st_resnetpico_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 7.54 | 132.63 | 3.0.0 | |
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| [st_resnetnano_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetnano_actrelu_pt_224/st_resnetnano_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 9.46 | 105.71 | 3.0.0 | |
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| [st_resnetmicro_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmicro_actrelu_pt_224/st_resnetmicro_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 13.83 | 72.31 | 3.0.0 | |
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| [st_resnetmilli_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmilli_actrelu_pt_224/st_resnetmilli_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 17.59 | 56.85 | 3.0.0 | |
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| [st_resnettiny_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnettiny_actrelu_pt_224/st_resnettiny_actrelu_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 24.10 | 41.49 | 3.0.0 | |
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| Model | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version | |
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|--------|--------|-------------|------------------|------------------|---------------------|-----------|----------------------|-------------------------| |
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| [st_resnetpico_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetpico_actrelu_pt_224/st_resnetpico_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 7.54 | 132.63 | 10.2.0 | 2.2.0 | |
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| [st_resnetnano_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetnano_actrelu_pt_224/st_resnetnano_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 9.46 | 105.71 | 10.2.0 | 2.2.0 | |
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| [st_resnetmicro_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmicro_actrelu_pt_224/st_resnetmicro_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 13.83 | 72.31 | 10.2.0 | 2.2.0 | |
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| [st_resnetmilli_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmilli_actrelu_pt_224/st_resnetmilli_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 17.59 | 56.85 | 10.2.0 | 2.2.0 | |
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| [st_resnettiny_actrelu_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnettiny_actrelu_pt_224/st_resnettiny_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 24.10 | 41.49 | 10.2.0 | 2.2.0 | |
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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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| [st_resnetmicro_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmicro_actrelu_pt_224/st_resnetmicro_actrelu_pt_224.onnx) | Float | 224×224×3 | 66.43 % | |
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| [st_resnetmicro_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmicro_actrelu_pt_224/st_resnetmicro_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 65.62 % | |
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| [st_resnetmilli_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmilli_actrelu_pt_224/st_resnetmilli_actrelu_pt_224.onnx) | Float | 224×224×3 | 71.10 % | |
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| [st_resnetmilli_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmilli_actrelu_pt_224/st_resnetmilli_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 70.45 % | |
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| [st_resnetnano_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetnano_actrelu_pt_224/st_resnetnano_actrelu_pt_224.onnx) | Float | 224×224×3 | 59.32 % | |
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| [st_resnetnano_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetnano_actrelu_pt_224/st_resnetnano_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 58.25 % | |
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| [st_resnetpico_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetpico_actrelu_pt_224/st_resnetpico_actrelu_pt_224.onnx) | Float | 224×224×3 | 49.42 % | |
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| [st_resnetpico_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetpico_actrelu_pt_224/st_resnetpico_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 46.98 % | |
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| [st_resnettiny_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnettiny_actrelu_pt_224/st_resnettiny_actrelu_pt_224.onnx) | Float | 224×224×3 | 72.07 % | |
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| [st_resnettiny_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnettiny_actrelu_pt_224/st_resnettiny_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 71.40 % | |
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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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| [st_resnetmicro_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmicro_actrelu_pt_224/st_resnetmicro_actrelu_pt_224.onnx) | Float | 224×224×3 | 66.43 % | |
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| [st_resnetmicro_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmicro_actrelu_pt_224/st_resnetmicro_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 65.62 % | |
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| [st_resnetmilli_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmilli_actrelu_pt_224/st_resnetmilli_actrelu_pt_224.onnx) | Float | 224×224×3 | 71.10 % | |
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| [st_resnetmilli_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetmilli_actrelu_pt_224/st_resnetmilli_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 70.45 % | |
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| [st_resnetnano_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetnano_actrelu_pt_224/st_resnetnano_actrelu_pt_224.onnx) | Float | 224×224×3 | 59.32 % | |
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| [st_resnetnano_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetnano_actrelu_pt_224/st_resnetnano_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 58.25 % | |
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| [st_resnetpico_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetpico_actrelu_pt_224/st_resnetpico_actrelu_pt_224.onnx) | Float | 224×224×3 | 49.42 % | |
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| [st_resnetpico_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnetpico_actrelu_pt_224/st_resnetpico_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 46.98 % | |
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| [st_resnettiny_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnettiny_actrelu_pt_224/st_resnettiny_actrelu_pt_224.onnx) | Float | 224×224×3 | 72.07 % | |
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| [st_resnettiny_actrelu_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/st_resnet_pt/ST_pretrainedmodel_public_dataset/Imagenet/st_resnettiny_actrelu_pt_224/st_resnettiny_actrelu_pt_224_qdq_int8.onnx) | Int8 | 224×224×3 | 71.40 % | |
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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**: STRESNET & STYOLO — https://arxiv.org/abs/2601.05364 |
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<a id="3">[3]</a> - **Model**: CompressNAS — https://arxiv.org/abs/2511.11716 |
