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mobilenetv1

Image Classification
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Update ST Model Zoo

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@@ -1,10 +1,3 @@
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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/image_classification/LICENSE.md
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- pipeline_tag: image-classification
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- ---
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  # MobileNet v1
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  ## **Use case** : `Image classification`
@@ -71,72 +64,71 @@ For an image resolution of NxM and P classes
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  - `tl` stands for "transfer learning", meaning that the model backbone weights were initialized from a pre-trained model, then only the last layer was unfrozen during the training.
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  - `fft` stands for "full fine-tuning", meaning that the full model weights were initialized from a transfer learning pre-trained model, and all the layers were unfrozen during the training.
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-
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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 v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 321.66 | 10.0.0 | 2.0.0 |
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- | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 1025.63 | 10.0.0 | 2.0.0 |
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- | [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 1568 | 0.0 | 3649.97 | 10.0.0 | 2.0.0 |
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- | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 549.88 | 10.0.0 | 2.0.0 |
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- | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 1478.58 | 10.0.0 | 2.0.0 |
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- | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 1568 | 0.0 | 4552.42 | 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 v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 2.83 | 353.36 | 10.0.0 | 2.0.0 |
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- | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 6.06 | 165.02 | 10.0.0 | 2.0.0 |
92
- | [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 16.94 | 59.03| 10.0.0 | 2.0.0 |
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- | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 3.57 | 280.11 | 10.0.0 | 2.0.0 |
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- | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 7.38 | 135.50 | 10.0.0 | 2.0.0 |
95
- | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 19.41 | 51.53 | 10.0.0 | 2.0.0 |
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  ### Reference **MCU** memory footprint based on Flowers dataset and ImageNet dataset (see Accuracy for details on dataset)
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- | Model | 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 v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 272.96 KiB | 16.38 KiB | 214.69 KiB | 68.05 KiB | 289.34 KiB | 282.74 KiB | 10.0.0 |
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- | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 449.58 KiB | 16.38 KiB | 812.61 KiB | 81.46 KiB | 465.96 KiB | 894.02 KiB | 10.0.0 |
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- | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | STM32H7 | 66.96 KiB | 16.33 KiB | 214.69 KiB | 68 KiB | 83.29 KiB | 282.69 KiB | 10.0.0 |
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- | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | STM32H7 | 52.8 KiB | 16.33 KiB | 214.55 KiB | 70.27 KiB | 69.13 KiB | 284.82 KiB | 10.0.0 |
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- | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 272.96 KiB | 16.43 KiB | 467.33 KiB | 70.02 KiB | 283.63 KiB | 537.35 KiB | 10.0.0 |
107
- | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 431.07 KiB | 16.43 KiB | 1314 KiB | 83.38 KiB | 447.5 KiB | 1397.38 KiB | 10.0.0 |
108
- | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 1331.13 KiB | 16.48 KiB | 4157.09 KiB | 110.11 KiB | 1347.61 KiB | 4267.2 KiB | 10.0.0 |
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110
 
111
  ### Reference **MCU** inference time based on Flowers dataset and ImageNet dataset (see Accuracy for details on dataset)
112
 
113
 
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  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
115
- |-------------------|--------|------------|------------------|------------------|-----------|------------------|-----------------------|
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- | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 163.78 ms | 10.0.0 |
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- | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 485.79 ms | 10.0.0 |
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- | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 29.94 ms | 10.0.0 |
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- | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | STM32H747I-DISCO | 1 CPU | 400 MHz | 28.34 ms | 10.0.0 |
120
- | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 166.75 ms | 10.0.0 |
121
- | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 504.37 ms | 10.0.0 |
122
- | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 1641.84 ms | 10.0.0 |
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124
 
125
  ### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset)
126
  | Model | 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 v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 14.29 ms | 6.04 | 93.96 | 0 | v5.1.0 | OpenVX |
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- | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 32.74 ms | 3.41 | 96.59 | 0 | v5.1.0 | OpenVX |
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- | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 3.740 ms | 14.20 | 85.80 | 0 | v5.1.0 | OpenVX |
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- | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 3.68 ms | 11.47 | 88.53 | 0 | v5.1.0 | OpenVX |
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- | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 33.97 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 91.42 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 6.40 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 5.83 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- |[MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 52.51 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- |[MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 145.4 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- |[MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 9.75 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
139
- |[MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 9.01 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
140
 
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  ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
142
 
@@ -169,7 +161,7 @@ Dataset details: [link](http://download.tensorflow.org/example_images/flower_pho
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  ### Accuracy with Plant-village dataset
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172
- Dataset details: [link](https://data.mendeley.com/datasets/tywbtsjrjv/1), License [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/), Quotation[[2]](#2) , Number of classes: 39, Number of images: 61 486
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  | Model | Format | Resolution | Top 1 Accuracy |
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  |-------|--------|------------|----------------|
@@ -190,7 +182,7 @@ Dataset details: [link](https://data.mendeley.com/datasets/tywbtsjrjv/1), Licens
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  ### Accuracy with Food-101 dataset
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193
- 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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  |-------|--------|------------|----------------|
@@ -212,7 +204,7 @@ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-1
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  ### Accuracy with ImageNet dataset
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215
- Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4)
216
  Number of classes: 1000.
217
  To perform the quantization, we calibrated the activations with a random subset of the training set.
218
  For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set.
 
 
 
 
 
 
 
 
1
  # MobileNet v1
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3
  ## **Use case** : `Image classification`
 
64
  - `tl` stands for "transfer learning", meaning that the model backbone weights were initialized from a pre-trained model, then only the last layer was unfrozen during the training.
65
  - `fft` stands for "full fine-tuning", meaning that the full model weights were initialized from a transfer learning pre-trained model, and all the layers were unfrozen during the training.
66
 
 
67
  ### Reference **NPU** memory footprint on food-101 and ImageNet dataset (see Accuracy for details on dataset)
68
  |Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version |
69
+ |----------|------------------|--------|-------------|------------------|------------------|---------------------|---------------|----------------------|-------------------------|
70
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 304.72 | 10.2.0 | 2.2.0 |
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+ | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 992.67 | 10.2.0 | 2.2.0 |
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+ | [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | 1568 | 0.0 | 3602.97 | 10.2.0 | 2.2.0 |
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+ | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 533.38 | 10.2.0 | 2.2.0 |
74
+ | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 1446.06 | 10.2.0 | 2.2.0 |
75
+ | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | 1568 | 0.0 | 4505.86 | 10.2.0 | 2.2.0 |
76
 
77
  ### Reference **NPU** inference time on food-101 and ImageNet dataset (see Accuracy for details on dataset)
78
 
79
 
80
+ | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
81
+ |--------|----------|--------|-------------|------------------|------------------|---------------------|-----------|----------------------|-------------------------|
82
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 2.81 | 355.87 | 10.2.0 | 2.2.0 |
83
+ | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 6.03 | 165.83 | 10.2.0 | 2.2.0 |
84
+ | [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 16.79 | 59.55 | 10.2.0 | 2.2.0 |
85
+ | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 3.56 | 280.89 | 10.2.0 | 2.2.0 |
86
+ | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 7.35 | 136.05 | 10.2.0 | 2.2.0 |
87
+ | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 19.26 | 51.93 | 10.2.0 | 2.2.0 |
88
 
89
 
90
  ### Reference **MCU** memory footprint based on Flowers dataset and ImageNet dataset (see Accuracy for details on dataset)
91
 
92
+ | Model | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
93
  |--------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------|
94
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 272.96 KiB | 16.38 KiB | 214.69 KiB | 67.24 KiB | 289.34 KiB | 281.93 KiB | 10.2.0 |
95
+ | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 449.58 KiB | 16.38 KiB | 812.61 KiB | 80.61 KiB | 465.96 KiB | 893.22 KiB | 10.2.0 |
96
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | STM32H7 | 66.96 KiB | 16.33 KiB | 214.69 KiB | 67.19 KiB | 83.29 KiB | 281.88 KiB | 10.2.0 |
97
+ | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | STM32H7 | 52.8 KiB | 16.33 KiB | 214.55 KiB | 69.28 KiB | 69.13 KiB | 283.83 KiB | 10.2.0 |
98
+ | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 267.2 KiB | 16.44 KiB | 467.33 KiB | 68.37 KiB | 283.64 KiB | 535.7 KiB | 10.2.0 |
99
+ | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 404.28 KiB | 16.44 KiB | 1314 KiB | 81.72 KiB | 447.51 KiB | 1395.72 KiB | 10.2.0 |
100
+ | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 1331.13 KiB | 16.48 KiB | 4157.09 KiB | 108.46 KiB | 1347.61 KiB | 4265.55 KiB | 10.2.0 |
101
 
102
 
103
  ### Reference **MCU** inference time based on Flowers dataset and ImageNet dataset (see Accuracy for details on dataset)
104
 
105
 
106
  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
107
+ |-------------------|--------|------------|------------------|------------------|-----------|---------------------|-----------------------|
108
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 166.9 ms | 10.2.0 |
109
+ | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 471.68 ms | 10.2.0 |
110
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 30.63 ms | 10.2.0 |
111
+ | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | STM32H747I-DISCO | 1 CPU | 400 MHz | 29.04 ms | 10.2.0 |
112
+ | [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 170.37 ms | 10.2.0 |
113
+ | [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 477.79 ms | 10.2.0 |
114
+ | [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 1656.41 ms | 10.2.0 |
115
 
116
 
117
  ### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset)
118
  | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
119
  |-----------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
120
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 14.27 ms | 7.54 | 92.46 | 0 | v6.1.0 | OpenVX |
121
+ | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 32.79 ms | 3.83 | 96.17 | 0 | v6.1.0 | OpenVX |
122
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 3.81 ms | 15.36 | 84.64 | 0 | v6.1.0 | OpenVX |
123
+ | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 3.66 ms | 13.91 | 86.09 | 0 | v6.1.0 | OpenVX |
124
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 33.91ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
125
+ | [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 90.6 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
126
+ | [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 6.32 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
127
+ | [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 5.83 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
128
+ |[MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 52.39 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
129
+ |[MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 144.47 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
130
+ |[MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 9.31 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
131
+ |[MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 9.37 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
132
 
133
  ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
134
 
 
161
  ### Accuracy with Plant-village dataset
162
 
163
 
164
+ Dataset details: [link](https://data.mendeley.com/datasets/tywbtsjrjv/1) , License [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/), Quotation[[2]](#2) , Number of classes: 39, Number of images: 61 486
165
 
166
  | Model | Format | Resolution | Top 1 Accuracy |
167
  |-------|--------|------------|----------------|
 
182
  ### Accuracy with Food-101 dataset
183
 
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
  |-------|--------|------------|----------------|
 
204
 
205
  ### Accuracy with ImageNet dataset
206
 
207
+ Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4).
208
  Number of classes: 1000.
209
  To perform the quantization, we calibrated the activations with a random subset of the training set.
210
  For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set.