| | --- |
| | license: apache-2.0 |
| | pipeline_tag: image-classification |
| | --- |
| | # MobileNet V2 |
| |
|
| | ## **Use case** : `Image classification` |
| |
|
| | # Model description |
| |
|
| |
|
| | MobileNetV2 improves upon V1 with **inverted residual blocks and linear bottlenecks**. It features skip connections between thin bottleneck layers, improving gradient flow and enabling deeper, more accurate networks. |
| |
|
| | The architecture uses **inverted residuals** that expand channels before depthwise convolution and then compress, with **linear bottlenecks** that remove non-linearity in narrow layers to preserve information. **Skip connections** between bottleneck layers improve gradient flow, typically with a 6x expansion ratio. |
| |
|
| | MobileNetV2 offers the best overall accuracy-efficiency trade-off with excellent quantization stability (typically <1% accuracy drop), making it ideal for production mobile applications, object detection backbones, and semantic segmentation networks. |
| |
|
| | (source: https://arxiv.org/abs/1801.04381) |
| |
|
| | The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment. |
| |
|
| | ## Network information |
| |
|
| |
|
| | | Network Information | Value | |
| | |--------------------|-------| |
| | | Framework | Torch | |
| | | MParams | ~1.49–3.72 M | |
| | | Quantization | Int8 | |
| | | Provenance | https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet | |
| | | Paper | https://arxiv.org/abs/1801.04381 | |
| |
|
| | ## Network inputs / outputs |
| |
|
| |
|
| | For an image resolution of NxM and P classes |
| |
|
| | | Input Shape | Description | |
| | | ----- | ----------- | |
| | | (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
| |
|
| | | Output Shape | Description | |
| | | ----- | ----------- | |
| | | (1, P) | Per-class confidence for P classes in FLOAT32| |
| |
|
| |
|
| | ## Recommended platforms |
| |
|
| |
|
| | | Platform | Supported | Recommended | |
| | |----------|-----------|-----------| |
| | | STM32L0 |[]|[]| |
| | | STM32L4 |[]|[]| |
| | | STM32U5 |[]|[]| |
| | | STM32H7 |[]|[]| |
| | | STM32MP1 |[]|[]| |
| | | STM32MP2 |[]|[]| |
| | | STM32N6 |[x]|[x]| |
| |
|
| | # Performances |
| |
|
| | ## Metrics |
| |
|
| | - Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option. |
| | - All the models are trained from scratch on Imagenet dataset |
| |
|
| | ### 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 | |
| | |-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------| |
| | | [mobilenetv2_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a025_pt_224/mobilenetv2_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 392 | 0 | 1522.31 | 3.0.0 | |
| | | [mobilenetv2b_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a025_pt_224/mobilenetv2b_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 392 | 0 | 1522.25 | 3.0.0 | |
| | | [mobilenetv2_w035_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_w035_pt_224/mobilenetv2_w035_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 931 | 0 | 1685.00 | 3.0.0 | |
| | | [mobilenetv2_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a050_pt_224/mobilenetv2_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1274 | 0 | 1972.03 | 3.0.0 | |
| | | [mobilenetv2b_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a050_pt_224/mobilenetv2b_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1065.75 | 0 | 1965.86 | 3.0.0 | |
| | | [mobilenetv2_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a075_pt_224/mobilenetv2_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1653.75 | 0 | 2737.58 | 3.0.0 | |
| | | [mobilenetv2b_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a075_pt_224/mobilenetv2b_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1653.75 | 0 | 2737.02 | 3.0.0 | |
| | | [mobilenetv2_a100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a100_pt_224/mobilenetv2_a100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2058 | 0 | 3813.97 | 3.0.0 | |
| | | [mobilenetv2b_a100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a100_pt_224/mobilenetv2b_a100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2058 | 0 | 3812.52 | 3.0.0 | |
| |
|
| |
|
| | ### Reference **NPU** inference time on Imagenet dataset (see Accuracy for details on dataset) |
| |
|
| | | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| | |--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------| |
| | | [mobilenetv2_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a025_pt_224/mobilenetv2_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 6.50 | 153.85 | 3.0.0 | |
| | | [mobilenetv2_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a050_pt_224/mobilenetv2_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 10.08 | 99.21 | 3.0.0 | |
| | | [mobilenetv2_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a075_pt_224/mobilenetv2_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 15.17 | 65.88 | 3.0.0 | |
| | | [mobilenetv2_a100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a100_pt_224/mobilenetv2_a100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 20.35 | 49.14 | 3.0.0 | |
| | | [mobilenetv2_w035_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_w035_pt_224/mobilenetv2_w035_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 8.58 | 116.55 | 3.0.0 | |
| | | [mobilenetv2b_a025_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a025_pt_224/mobilenetv2b_a025_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 6.29 | 158.98 | 3.0.0 | |
| | | [mobilenetv2b_a050_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a050_pt_224/mobilenetv2b_a050_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 9.79 | 102.14 | 3.0.0 | |
| | | [mobilenetv2b_a075_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a075_pt_224/mobilenetv2b_a075_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 14.56 | 68.68 | 3.0.0 | |
| | | [mobilenetv2b_a100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a100_pt_224/mobilenetv2b_a100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 20.39 | 49.04 | 3.0.0 | |
| |
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|
| |
|
| | ### Accuracy with Imagenet dataset |
| |
|
| | | model | Format | Resolution | Top 1 Accuracy | |
| | | --- | --- | --- | --- | |
| | | [mobilenetv2_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a025_pt_224/mobilenetv2_a025_pt_224.onnx) | Float | 224x224x3 | 52.29 % | |
| | | [mobilenetv2_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a025_pt_224/mobilenetv2_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 51.51 % | |
| | | [mobilenetv2_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a050_pt_224/mobilenetv2_a050_pt_224.onnx) | Float | 224x224x3 | 66.20 % | |
| | | [mobilenetv2_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a050_pt_224/mobilenetv2_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 65.31 % | |
| | | [mobilenetv2_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a075_pt_224/mobilenetv2_a075_pt_224.onnx) | Float | 224x224x3 | 70.78 % | |
| | | [mobilenetv2_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a075_pt_224/mobilenetv2_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 70.33 % | |
| | | [mobilenetv2_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a100_pt_224/mobilenetv2_a100_pt_224.onnx) | Float | 224x224x3 | 73.17 % | |
| | | [mobilenetv2_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a100_pt_224/mobilenetv2_a100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 72.76 % | |
| | | [mobilenetv2_w035_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_w035_pt_224/mobilenetv2_w035_pt_224.onnx) | Float | 224x224x3 | 61.02 % | |
| | | [mobilenetv2_w035_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_w035_pt_224/mobilenetv2_w035_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 60.09 % | |
| | | [mobilenetv2b_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a025_pt_224/mobilenetv2b_a025_pt_224.onnx) | Float | 224x224x3 | 53.53 % | |
| | | [mobilenetv2b_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a025_pt_224/mobilenetv2b_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 52.55 % | |
| | | [mobilenetv2b_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a050_pt_224/mobilenetv2b_a050_pt_224.onnx) | Float | 224x224x3 | 66.30 % | |
| | | [mobilenetv2b_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a050_pt_224/mobilenetv2b_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 65.67 % | |
| | | [mobilenetv2b_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a075_pt_224/mobilenetv2b_a075_pt_224.onnx) | Float | 224x224x3 | 70.41 % | |
| | | [mobilenetv2b_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a075_pt_224/mobilenetv2b_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 70.20 % | |
| | | [mobilenetv2b_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a100_pt_224/mobilenetv2b_a100_pt_224.onnx) | Float | 224x224x3 | 73.33 % | |
| | | [mobilenetv2b_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a100_pt_224/mobilenetv2b_a100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 72.89 % | |
| |
|
| |
|
| | | model | Format | Resolution | Top 1 Accuracy | |
| | | --- | --- | --- | --- | |
| | | [mobilenetv2_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a025_pt_224/mobilenetv2_a025_pt_224.onnx) | Float | 224x224x3 | 52.29 % | |
| | | [mobilenetv2_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a025_pt_224/mobilenetv2_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 51.51 % | |
| | | [mobilenetv2_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a050_pt_224/mobilenetv2_a050_pt_224.onnx) | Float | 224x224x3 | 66.20 % | |
| | | [mobilenetv2_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a050_pt_224/mobilenetv2_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 65.31 % | |
| | | [mobilenetv2_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a075_pt_224/mobilenetv2_a075_pt_224.onnx) | Float | 224x224x3 | 70.78 % | |
| | | [mobilenetv2_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a075_pt_224/mobilenetv2_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 70.33 % | |
| | | [mobilenetv2_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a100_pt_224/mobilenetv2_a100_pt_224.onnx) | Float | 224x224x3 | 73.17 % | |
| | | [mobilenetv2_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_a100_pt_224/mobilenetv2_a100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 72.76 % | |
| | | [mobilenetv2_w035_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_w035_pt_224/mobilenetv2_w035_pt_224.onnx) | Float | 224x224x3 | 61.02 % | |
| | | [mobilenetv2_w035_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2_w035_pt_224/mobilenetv2_w035_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 60.09 % | |
| | | [mobilenetv2b_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a025_pt_224/mobilenetv2b_a025_pt_224.onnx) | Float | 224x224x3 | 53.53 % | |
| | | [mobilenetv2b_a025_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a025_pt_224/mobilenetv2b_a025_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 52.55 % | |
| | | [mobilenetv2b_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a050_pt_224/mobilenetv2b_a050_pt_224.onnx) | Float | 224x224x3 | 66.30 % | |
| | | [mobilenetv2b_a050_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a050_pt_224/mobilenetv2b_a050_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 65.67 % | |
| | | [mobilenetv2b_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a075_pt_224/mobilenetv2b_a075_pt_224.onnx) | Float | 224x224x3 | 70.41 % | |
| | | [mobilenetv2b_a075_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a075_pt_224/mobilenetv2b_a075_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 70.20 % | |
| | | [mobilenetv2b_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a100_pt_224/mobilenetv2b_a100_pt_224.onnx) | Float | 224x224x3 | 73.33 % | |
| | | [mobilenetv2b_a100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2_pt/Public_pretrainedmodel_public_dataset/Imagenet/mobilenetv2b_a100_pt_224/mobilenetv2b_a100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 72.89 % | |
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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**: MobileNetV2 — https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet |
