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resnet50v2

Image Classification
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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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  # ResNet50 v2
@@ -70,22 +70,22 @@ 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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- | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | | | | 10.0.0 | 2.0.0 |
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- | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | | | | 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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- | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | | | 10.0.0 | 2.0.0 |
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- | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | | | 10.0.0 | 2.0.0 |
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  ### Reference **MCU** memory footprint based on Food-101 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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- | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 2142.07 KiB | 41.02 KiB | 23240.96 KiB | 226.05 KiB | 2183.09 KiB | 23467.01 KiB | 10.0.0 |
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- | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 2142.07 KiB | 41.02 KiB | 25042.47 KiB | 226.05 KiB | 2183.09 KiB | 25268.52 KiB | 10.0.0 |
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  ### Reference **MCU** inference time based on Food-101 and ImageNet dataset (see Accuracy for details on dataset)
@@ -93,8 +93,8 @@ For an image resolution of NxM and P classes
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  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
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  |-------------------|--------|------------|------------------|------------------|-----------|------------------|-----------------------|
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- | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 11354.82 ms | 10.0.0 |
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- | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 11368.81 ms | 10.0.0 |
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@@ -104,8 +104,8 @@ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-1
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  | Model | Format | Resolution | Top 1 Accuracy |
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  |-------|--------|------------|----------------|
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- | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft.h5) | Float | 224x224x3 | 71.53 % |
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- | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | 70.07 % |
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  ### Accuracy with ImageNet dataset
@@ -117,8 +117,8 @@ For the sake of simplicity, the accuracy reported here was estimated on the 5000
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  |model | Format | Resolution | Top 1 Accuracy |
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  |---------|--------|------------|----------------|
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- | [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224.h5) | Float | 224x224x3 | 66.38 % |
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- | [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | 65.99 % |
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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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  # ResNet50 v2
 
70
  ### Reference **NPU** memory footprint on food-101 and ImageNet dataset (see Accuracy for details on dataset)
71
  |Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version |
72
  |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
73
+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | | | | 10.0.0 | 2.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | | | | 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)
77
  | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
78
  |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
79
+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | | | 10.0.0 | 2.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | | | 10.0.0 | 2.0.0 |
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  ### Reference **MCU** memory footprint based on Food-101 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 |
86
  |--------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------|
87
+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 2142.07 KiB | 41.02 KiB | 23240.96 KiB | 226.05 KiB | 2183.09 KiB | 23467.01 KiB | 10.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 2142.07 KiB | 41.02 KiB | 25042.47 KiB | 226.05 KiB | 2183.09 KiB | 25268.52 KiB | 10.0.0 |
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  ### Reference **MCU** inference time based on Food-101 and ImageNet dataset (see Accuracy for details on dataset)
 
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  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
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  |-------------------|--------|------------|------------------|------------------|-----------|------------------|-----------------------|
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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 11354.82 ms | 10.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 11368.81 ms | 10.0.0 |
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  | Model | Format | Resolution | Top 1 Accuracy |
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  |-------|--------|------------|----------------|
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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft.h5) | Float | 224x224x3 | 71.53 % |
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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | 70.07 % |
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  ### Accuracy with ImageNet dataset
 
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  |model | Format | Resolution | Top 1 Accuracy |
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  |---------|--------|------------|----------------|
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+ | [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224.h5) | Float | 224x224x3 | 66.38 % |
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+ | [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | 65.99 % |
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