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mobilenetv2

Image Classification
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Update ST Model Zoo

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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/tree/main/image_classification/LICENSE.md
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- pipeline_tag: image-classification
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- ---
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  # MobileNet v2
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  ## **Use case** : `Image classification`
@@ -82,67 +75,67 @@ For an image resolution of NxM and P classes
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  ### Reference **NPU** memory footprint on food-101 and ImageNet dataset (see Accuracy for details on dataset)
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  |Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version |
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- |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | food-101 | Int8 | 128x128x3 | STM32N6 | 240 | 0.0 | 715.67 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 980 | 0.0 | 730.7 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 2058 | 0.0 | 3110.05 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35 fft](ST_pretrainedmodel_public_dataset/person/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Person | Int8 | 128x128x3 | STM32N6 | 240 | 0.0 | 589.45 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32N6 | 240 | 0.0 | 1840.94 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 980 | 0.0 | 1855.97 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 2058 | 0.0 | 4235.31 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 2361 | 0.0 | 7315.69 | 10.0.0 | 2.0.0 |
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  ### Reference **NPU** inference time on food-101 and ImageNet dataset (see Accuracy for details on dataset)
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- | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
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- |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | food-101 | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 3.33 | 300.30 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 6.12 | 163.40 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 18.08 | 55.32 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35 fft](ST_pretrainedmodel_public_dataset/person/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Person | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 2.99 | 334.45 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 6.35 | 157.48 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 9.14 | 109.40 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 21.08 | 47.44 | 10.0.0 | 2.0.0 |
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- | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 35.34 | 28.30 | 10.0.0 | 2.0.0 |
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  ### Reference **MCU** memory footprint based on Flowers and ImageNet dataset (see Accuracy for details on dataset)
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- | Model | Dataset | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
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- |--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|-------------|---------|----------------|-------------|---------------|------------|------------|-------------|----------------------|
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | STM32H7 | 237.32 KiB | 30.14 KiB | 406.86 KiB | 108.29 KiB | 267.46 KiB | 515.15 KiB | 10.0.0 |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | STM32H7 | 832.64 KiB | 30.19 KiB | 406.86 KiB | 108.40 KiB | 862.83 KiB | 515.26 KiB | 10.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32H7 | 237.32 KiB | 30.14 KiB | 1654.5 KiB KiB | 108.29 KiB | 267.46 KiB | 1762.79 KiB | 10.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H7 | 832.64 KiB | 30.19 KiB | 1654.5 KiB | 108.40 KiB | 862.83 KiB | 1762.9 KiB | 10.0.0 |
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- | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H7 | 1727.02 KiB | 30.19 KiB | 3458.97 KiB | 157.37 KiB | 1757.21 KiB | 3616.34 KiB | 10.0.0 |
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- | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H7 | 2332.2 KiB | 30.19 KiB | 6015.34 KiB | 191.16 KiB | 2362.39 KiB | 6206.53 KiB | 10.0.0 |
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  ### Reference **MCU** inference time based on Flowers and ImageNet dataset (see Accuracy for details on dataset)
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- | Model | Dataset | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
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  |--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|-------------|------------------|------------------|-------------|---------------------|-----------------------|
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 94.34 ms | 10.0.0 |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 290.75 ms | 10.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 116.13 ms | 10.0.0 |
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- | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 313.92 ms | 10.0.0 |
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- | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 1106.64 ms | 10.0.0 |
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- | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 2010.66 ms | 10.0.0 |
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  ### Reference **MPU** inference time based on Flowers and ImageNet dataset (see Accuracy for details on dataset)
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  | Model | Dataset | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
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  |--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
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- | [MobileNet v2 1.0_per_tensor](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8_per_tensor.tflite) | ImageNet | Int8 | 224x224x3 | per-tensor | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 11.92 ms | 92.74 | 7.26 |0 | v5.1.0 | OpenVX |
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- | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 76.29 ms | 3.13 | 96.87 |0 | v5.1.0 | OpenVX |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 25.51 ms | 4.37 | 95.63 |0 | v5.1.0 | OpenVX |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 9.14 ms | 12.06 | 87.94 |0 | v5.1.0 | OpenVX |
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- | [MobileNet v2 1.0_per_tensor](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8_per_tensor.tflite) | ImageNet | Int8 | 224x224x3 | per-tensor | STM32MP157F-DK2 | 2 CPU | 800 MHz | 332.9 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 194.1 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 54.52 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
141
- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 17.16 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
142
- | [MobileNet v2 1.0_per_tensor](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8_per_tensor.tflite) | ImageNet | Int8 | 224x224x3 | per-tensor | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 415.7 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
143
- | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 308.80 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 54.85 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 27.17 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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  ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
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  ### Accuracy with Flowers dataset
@@ -189,7 +182,7 @@ Dataset details: [link](https://data.mendeley.com/datasets/tywbtsjrjv/1) , Licen
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  ### Accuracy with Food-101 dataset
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- Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) , Quotation[[3]](#3) , Number of classes: 101 , Number of images: 101 000
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  | Model | Format | Resolution | Top 1 Accuracy |
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  |-------|--------|------------|----------------|
@@ -197,13 +190,13 @@ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-1
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  | [MobileNet v2 0.35 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_tfs/mobilenet_v2_0.35_128_tfs_int8.tflite) | Int8 | 128x128x3 | 63.41 % |
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  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_tl/mobilenet_v2_0.35_128_tl.h5) | Float | 128x128x3 | 44.74 % |
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  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_tl/mobilenet_v2_0.35_128_tl_int8.tflite) | Int8 | 128x128x3 | 42.01 % |
200
- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft.h5) | Float | 128x128x3 | 64.22 % |
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  | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Int8 | 128x128x3 | 63.41 % |
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  | [MobileNet v2 0.35 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tfs/mobilenet_v2_0.35_224_tfs.h5) | Float | 224x224x3 | 72.31 % |
203
  | [MobileNet v2 0.35 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tfs/mobilenet_v2_0.35_224_tfs_int8.tflite) | Int8 | 224x224x3 | 72.05 % |
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  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tl/mobilenet_v2_0.35_224_tl.h5) | Float | 224x224x3 | 49.01 % |
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  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tl/mobilenet_v2_0.35_224_tl_int8.tflite) | Int8 | 224x224x3 | 47.26 % |
206
- | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft.h5) | Float | 224x224x3 | 73.76 % |
207
  | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Int8 | 224x224x3 | 73.16 % |
208
  | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft.h5) | Float | 224x224x3 | 77.77 % |
209
  | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft_int8.tflite) | Int8 | 224x224x3 | 77.09 % |
@@ -226,7 +219,7 @@ Dataset details: [link](https://cocodataset.org/) , License [Creative Commons At
226
 
227
  ### Accuracy with ImageNet
228
 
229
- Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4)
230
  Number of classes: 1000.
231
  To perform the quantization, we calibrated the activations with a random subset of the training set.
232
  For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set.
@@ -262,4 +255,4 @@ L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative C
262
 
263
  <a id="4">[4]</a>
264
  Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.
265
- (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge.
 
 
 
 
 
 
 
 
1
  # MobileNet v2
2
 
3
  ## **Use case** : `Image classification`
 
75
 
76
  ### Reference **NPU** memory footprint on food-101 and ImageNet dataset (see Accuracy for details on dataset)
77
  |Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version |
78
+ |----------|------------------|--------|-------------|------------------|------------------|---------------------|---------------|----------------------|-------------------------|
79
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | food-101 | Int8 | 128x128x3 | STM32N6 | 240 | 0.0 | 680.92 | 10.2.0 | 2.2.0 |
80
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 980 | 0.0 | 695.95 | 10.2.0 | 2.2.0 |
81
+ | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 2058 | 0.0 | 3070.61 | 10.2.0 | 2.2.0 |
82
+ | [MobileNet v2 0.35 fft](ST_pretrainedmodel_public_dataset/person/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Person | Int8 | 128x128x3 | STM32N6 | 240 | 0.0 | 554.94 | 10.2.0 | 2.2.0 |
83
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32N6 | 240 | 0.0 | 1806.61 | 10.2.0 | 2.2.0 |
84
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 980 | 0.0 | 1821.64 | 10.2.0 | 2.2.0 |
85
+ | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 2058 | 0.0 | 4196.3 | 10.2.0 | 2.2.0 |
86
+ | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 2361 | 0.0 | 7285.86 | 10.2.0 | 2.2.0 |
87
 
88
 
89
  ### Reference **NPU** inference time on food-101 and ImageNet dataset (see Accuracy for details on dataset)
90
+ | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
91
+ |--------|------------------|--------|-------------|------------------|------------------|---------------------|-----------|----------------------|-------------------------|
92
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | food-101 | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 3.34 | 299.4 | 10.2.0 | 2.2.0 |
93
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 6.03 | 165.83 | 10.2.0 | 2.2.0 |
94
+ | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 17.31 | 57.77 | 10.2.0 | 2.2.0 |
95
+ | [MobileNet v2 0.35 fft](ST_pretrainedmodel_public_dataset/person/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Person | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 2.95 | 338.98 | 10.2.0 | 2.2.0 |
96
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 6.37 | 156.98 | 10.2.0 | 2.2.0 |
97
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 9.06 | 110.37 | 10.2.0 | 2.2.0 |
98
+ | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 20.3 | 49.26 | 10.2.0 | 2.2.0 |
99
+ | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 33.8 | 29.95 | 10.2.0 | 2.2.0 |
100
 
101
 
102
  ### Reference **MCU** memory footprint based on Flowers and ImageNet dataset (see Accuracy for details on dataset)
103
 
104
+ | Model | Dataset | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
105
+ |--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|-------------|---------|----------------|-------------|---------------|------------|-------------|-------------|----------------------|
106
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | STM32H7 | 237.32 KiB | 30.15 KiB | 406.86 KiB | 107.4 KiB | 267.46 KiB | 514.26 KiB | 10.2.0 |
107
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | STM32H7 | 832.64 KiB | 30.2 KiB | 406.86 KiB | 107.52 KiB | 862.84 KiB | 514.38 KiB | 10.2.0 |
108
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32H7 | 237.32 KiB | 30.15 KiB | 1654.5 KiB KiB | 107.4 KiB | 267.47 KiB | 1762.79 KiB | 10.2.0 |
109
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H7 | 832.64 KiB | 30.2 KiB | 1654.5 KiB | 107.52 KiB | 862.84 KiB | 1762.9 KiB | 10.2.0 |
110
+ | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H7 | 1727.02 KiB | 30.2 KiB | 3458.97 KiB | 157.37 KiB | 1757.22 KiB | 3616.34 KiB | 10.2.0 |
111
+ | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H7 | 2332.2 KiB | 30.2 KiB | 6015.34 KiB | 191.16 KiB | 2362.39 KiB | 6206.53 KiB | 10.2.0 |
112
 
113
  ### Reference **MCU** inference time based on Flowers and ImageNet dataset (see Accuracy for details on dataset)
114
 
115
+ | Model | Dataset | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
116
  |--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|-------------|------------------|------------------|-------------|---------------------|-----------------------|
117
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 96.52 ms | 10.2.0 |
118
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 297.38 ms | 10.2.0 |
119
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_128/mobilenet_v2_0.35_128_int8.tflite) | ImageNet | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 100.66 ms | 10.2.0 |
120
+ | [MobileNet v2 0.35](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_0.35_224/mobilenet_v2_0.35_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 301.58 ms | 10.2.0 |
121
+ | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 1124.79 ms | 10.2.0 |
122
+ | [MobileNet v2 1.4](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 2038.28 ms | 10.2.0 |
123
 
124
  ### Reference **MPU** inference time based on Flowers and ImageNet dataset (see Accuracy for details on dataset)
125
  | Model | Dataset | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
126
  |--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
127
+ | [MobileNet v2 1.0_per_tensor](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8_per_tensor.tflite) | ImageNet | Int8 | 224x224x3 | per-tensor | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 12.39 ms | 81.42 | 18.58 |0 | v6.1.0 | OpenVX |
128
+ | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 76.19 ms | 2.61 | 97.39 |0 | v6.1.0 | OpenVX |
129
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 25.5 ms | 4.31 | 95.69 |0 | v6.1.0 | OpenVX |
130
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 9.12 ms | 12.45 | 87.55 |0 | v6.1.0 | OpenVX |
131
+ | [MobileNet v2 1.0_per_tensor](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8_per_tensor.tflite) | ImageNet | Int8 | 224x224x3 | per-tensor | STM32MP157F-DK2 | 2 CPU | 800 MHz | 331.69 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
132
+ | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 193.84 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
133
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 54.15 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
134
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 17.44 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
135
+ | [MobileNet v2 1.0_per_tensor](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8_per_tensor.tflite) | ImageNet | Int8 | 224x224x3 | per-tensor | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 418.33 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
136
+ | [MobileNet v2 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224_int8.tflite) | ImageNet | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 310.64 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
137
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Flowers | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 84.39 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
138
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Flowers | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 26.85 ms | NA | NA |100 | v6.1.0 | TensorFlowLite 2.18.0 |
139
 
140
  ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
141
  ### Accuracy with Flowers dataset
 
182
 
183
  ### Accuracy with Food-101 dataset
184
 
185
+ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/), Quotation[[3]](#3) , Number of classes: 101 , Number of images: 101 000
186
 
187
  | Model | Format | Resolution | Top 1 Accuracy |
188
  |-------|--------|------------|----------------|
 
190
  | [MobileNet v2 0.35 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_tfs/mobilenet_v2_0.35_128_tfs_int8.tflite) | Int8 | 128x128x3 | 63.41 % |
191
  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_tl/mobilenet_v2_0.35_128_tl.h5) | Float | 128x128x3 | 44.74 % |
192
  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_tl/mobilenet_v2_0.35_128_tl_int8.tflite) | Int8 | 128x128x3 | 42.01 % |
193
+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft.h5) | Float | 128x128x3 | 64.21 % |
194
  | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft_int8.tflite) | Int8 | 128x128x3 | 63.41 % |
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  | [MobileNet v2 0.35 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tfs/mobilenet_v2_0.35_224_tfs.h5) | Float | 224x224x3 | 72.31 % |
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  | [MobileNet v2 0.35 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tfs/mobilenet_v2_0.35_224_tfs_int8.tflite) | Int8 | 224x224x3 | 72.05 % |
197
  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tl/mobilenet_v2_0.35_224_tl.h5) | Float | 224x224x3 | 49.01 % |
198
  | [MobileNet v2 0.35 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_tl/mobilenet_v2_0.35_224_tl_int8.tflite) | Int8 | 224x224x3 | 47.26 % |
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+ | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft.h5) | Float | 224x224x3 | 73.74 % |
200
  | [MobileNet v2 0.35 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_0.35_224_fft/mobilenet_v2_0.35_224_fft_int8.tflite) | Int8 | 224x224x3 | 73.16 % |
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  | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft.h5) | Float | 224x224x3 | 77.77 % |
202
  | [MobileNet v2 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv2/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v2_1.0_224_fft/mobilenet_v2_1.0_224_fft_int8.tflite) | Int8 | 224x224x3 | 77.09 % |
 
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  ### Accuracy with ImageNet
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+ Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4).
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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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  <a id="4">[4]</a>
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  Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.
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+ (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge.