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@@ -2,7 +2,7 @@
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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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  ---
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  # EfficientNet v2
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@@ -77,27 +77,27 @@ 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)
78
  |Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
79
  |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
80
- | [efficientnet_v2B0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft_qdq_int8.onnx) | food-101 | Int8 | 224x224x3 | STM32N6 | 1834.44 |0.0| 7553.77 | 10.0.0 | 2.0.0 |
81
- | [efficientnet_v2B1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft_qdq_int8.onnx) | food-101 | Int8 | 240x240x3 | STM32N6 | 2589.97 |0.0| 8924.78 | 10.0.0 | 2.0.0 |
82
- | [efficientnet_v2B2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft_qdq_int8.onnx) | food-101 | Int8 | 260x260x3 | STM32N6 | 2629.56 |528.12| 11212.75| 10.0.0 | 2.0.0 |
83
- | [efficientnet_v2S_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft_qdq_int8.onnx) | food-10 | Int8 | 384x384x3 | STM32N6 | 2700 | 6912 | 25756.92 | 10.0.0 | 2.0.0 |
84
- | [efficientnet_v2B0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224_qdq_int8.onnx) | ImageNet | Int8 | 224x224x3 | STM32N6 | 1834.44 | 0.0 | 8680.39 | 10.0.0 | 2.0.0 |
85
- | [efficientnet_v2B1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240_qdq_int8.onnx) | ImageNet | Int8 | 240x240x3 | STM32N6 | 2589.97 | 0.0 | 10051.7 | 10.0.0 | 2.0.0 |
86
- | [efficientnet_v2B2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260_qdq_int8.onnx) | ImageNet | Int8 | 260x260x3 | STM32N6 | 2629.56 | 528.12 | 12451.77 | 10.0.0 | 2.0.0 |
87
- | [efficientnet_v2S_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384_qdq_int8.onnx) | ImageNet | Int8 | 384x384x3 | STM32N6 | 2700 | 6912 | 26884.47 | 10.0.0 | 2.0.0 |
88
 
89
 
90
  ### Reference **NPU** inference time on food-101 and ImageNet dataset (see Accuracy for details on dataset)
91
  | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
92
  |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
93
- | [efficientnet_v2B0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft_qdq_int8.onnx) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 54.32 | 18.41 | 10.0.0 | 2.0.0 |
94
- | [efficientnet_v2B1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft_qdq_int8.onnx) | food-101 | Int8 | 240x240x3 | STM32N6570-DK | NPU/MCU | 73.89 | 13.53 | 10.0.0 | 2.0.0 |
95
- | [efficientnet_v2B2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft_qdq_int8.onnx) | food-101 | Int8 | 260x260x3 | STM32N6570-DK | NPU/MCU | 146.01 | 6.85 | 10.0.0 | 2.0.0 |
96
- | [efficientnet_v2S_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft_qdq_int8.onnx) | food-101 | Int8 | 384x384x3 | STM32N6570-DK | NPU/MCU | 842 | 1.19 | 10.0.0 | 2.0.0 |
97
- | [efficientnet_v2B0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224_qdq_int8.onnx) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 57.5 | 17.39 | 10.0.0 | 2.0.0 |
98
- | [efficientnet_v2B1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240_qdq_int8.onnx) | ImageNet | Int8 | 240x240x3 | STM32N6570-DK | NPU/MCU | 77.25 | 12.94 | 10.0.0 | 2.0.0 |
99
- | [efficientnet_v2B2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260_qdq_int8.onnx) | ImageNet | Int8 | 260x260x3 | STM32N6570-DK | NPU/MCU | 148.78 | 6.72 | 10.0.0 | 2.0.0 |
100
- | [efficientnet_v2S_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384_qdq_int8.onnx) | ImageNet | Int8 | 384x384x3 | STM32N6570-DK | NPU/MCU | 809.73 | 1.23 | 10.0.0 | 2.0.0 |
101
 
102
  * The deployment of all the models listed in the table is supported, except for the efficientnet_v2S_384 model, for which support is coming soon.
103
  ### Accuracy with Food-101 dataset
@@ -106,14 +106,14 @@ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-1
106
 
107
  | Model | Format | Resolution | Top 1 Accuracy |
108
  |--------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|----------------|
109
- | [efficientnet_v2B0_224_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft.h5) | Float | 224x224x3 | 81.35 % |
110
- | [efficientnet_v2B0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft_qdq_int8.onnx) | Int8 | 224x224x3 | 81.1 % |
111
- | [efficientnet_v2B1_240_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft.h5) | Float | 240x240x3 | 83.23 % |
112
- | [efficientnet_v2B1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft_qdq_int8.onnx) | Int8 | 240x240x3 | 82.95 % |
113
- | [efficientnet_v2B2_260_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft.h5) | Float | 260x260x3 | 84.37 % |
114
- | [efficientnet_v2B2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft_qdq_int8.onnx) | Int8 | 260x260x3 | 84.04 % |
115
- | [efficientnet_v2S_384_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft.h5) | Float | 384x384x3 | 88.16 % |
116
- | [efficientnet_v2S_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft_qdq_int8.onnx) | Int8 | 384x384x3 | 87.34 % |
117
 
118
 
119
  ### Accuracy with ImageNet
@@ -125,14 +125,14 @@ For the sake of simplicity, the accuracy reported here was estimated on the 1000
125
 
126
  | Model | Format | Resolution | Top 1 Accuracy |
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  |------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|----------------|
128
- | [efficientnet_v2B0_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224.h5) | Float | 224x224x3 | 73.94 % |
129
- | [efficientnet_v2B0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224_qdq_int8.onnx) | Int8 | 224x224x3 | 72.21 % |
130
- | [efficientnet_v2B1_240](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240.h5) | Float | 240x240x3 | 76.14 % |
131
- | [efficientnet_v2B1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240_qdq_int8.onnx) | Int8 | 240x240x3 | 75.5 % |
132
- | [efficientnet_v2B2_260](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260.h5) | Float | 260x260x3 | 76.58 % |
133
- | [efficientnet_v2B2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260_qdq_int8.onnx) | Int8 | 260x260x3 | 76.26 % |
134
  | [efficientnet_v2S_384](./Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384.h5) | Float | 384x384x3 | 83.52 % |
135
- | [efficientnet_v2S_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384_qdq_int8.onnx) | Int8 | 384x384x3 | 83.07 % |
136
 
137
 
138
  ## Retraining and Integration in a simple example:
 
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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
6
  ---
7
  # EfficientNet v2
8
 
 
77
  ### Reference **NPU** memory footprint on food-101 and ImageNet dataset (see Accuracy for details on dataset)
78
  |Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
79
  |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
80
+ | [efficientnet_v2B0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft_qdq_int8.onnx) | food-101 | Int8 | 224x224x3 | STM32N6 | 1834.44 |0.0| 7553.77 | 10.0.0 | 2.0.0 |
81
+ | [efficientnet_v2B1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft_qdq_int8.onnx) | food-101 | Int8 | 240x240x3 | STM32N6 | 2589.97 |0.0| 8924.78 | 10.0.0 | 2.0.0 |
82
+ | [efficientnet_v2B2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft_qdq_int8.onnx) | food-101 | Int8 | 260x260x3 | STM32N6 | 2629.56 |528.12| 11212.75| 10.0.0 | 2.0.0 |
83
+ | [efficientnet_v2S_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft_qdq_int8.onnx) | food-10 | Int8 | 384x384x3 | STM32N6 | 2700 | 6912 | 25756.92 | 10.0.0 | 2.0.0 |
84
+ | [efficientnet_v2B0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224_qdq_int8.onnx) | ImageNet | Int8 | 224x224x3 | STM32N6 | 1834.44 | 0.0 | 8680.39 | 10.0.0 | 2.0.0 |
85
+ | [efficientnet_v2B1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240_qdq_int8.onnx) | ImageNet | Int8 | 240x240x3 | STM32N6 | 2589.97 | 0.0 | 10051.7 | 10.0.0 | 2.0.0 |
86
+ | [efficientnet_v2B2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260_qdq_int8.onnx) | ImageNet | Int8 | 260x260x3 | STM32N6 | 2629.56 | 528.12 | 12451.77 | 10.0.0 | 2.0.0 |
87
+ | [efficientnet_v2S_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384_qdq_int8.onnx) | ImageNet | Int8 | 384x384x3 | STM32N6 | 2700 | 6912 | 26884.47 | 10.0.0 | 2.0.0 |
88
 
89
 
90
  ### Reference **NPU** inference time on food-101 and ImageNet dataset (see Accuracy for details on dataset)
91
  | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
92
  |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
93
+ | [efficientnet_v2B0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft_qdq_int8.onnx) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 54.32 | 18.41 | 10.0.0 | 2.0.0 |
94
+ | [efficientnet_v2B1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft_qdq_int8.onnx) | food-101 | Int8 | 240x240x3 | STM32N6570-DK | NPU/MCU | 73.89 | 13.53 | 10.0.0 | 2.0.0 |
95
+ | [efficientnet_v2B2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft_qdq_int8.onnx) | food-101 | Int8 | 260x260x3 | STM32N6570-DK | NPU/MCU | 146.01 | 6.85 | 10.0.0 | 2.0.0 |
96
+ | [efficientnet_v2S_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft_qdq_int8.onnx) | food-101 | Int8 | 384x384x3 | STM32N6570-DK | NPU/MCU | 842 | 1.19 | 10.0.0 | 2.0.0 |
97
+ | [efficientnet_v2B0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224_qdq_int8.onnx) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 57.5 | 17.39 | 10.0.0 | 2.0.0 |
98
+ | [efficientnet_v2B1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240_qdq_int8.onnx) | ImageNet | Int8 | 240x240x3 | STM32N6570-DK | NPU/MCU | 77.25 | 12.94 | 10.0.0 | 2.0.0 |
99
+ | [efficientnet_v2B2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260_qdq_int8.onnx) | ImageNet | Int8 | 260x260x3 | STM32N6570-DK | NPU/MCU | 148.78 | 6.72 | 10.0.0 | 2.0.0 |
100
+ | [efficientnet_v2S_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384_qdq_int8.onnx) | ImageNet | Int8 | 384x384x3 | STM32N6570-DK | NPU/MCU | 809.73 | 1.23 | 10.0.0 | 2.0.0 |
101
 
102
  * The deployment of all the models listed in the table is supported, except for the efficientnet_v2S_384 model, for which support is coming soon.
103
  ### Accuracy with Food-101 dataset
 
106
 
107
  | Model | Format | Resolution | Top 1 Accuracy |
108
  |--------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|----------------|
109
+ | [efficientnet_v2B0_224_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft.h5) | Float | 224x224x3 | 81.35 % |
110
+ | [efficientnet_v2B0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B0_224_fft/efficientnet_v2B0_224_fft_qdq_int8.onnx) | Int8 | 224x224x3 | 81.1 % |
111
+ | [efficientnet_v2B1_240_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft.h5) | Float | 240x240x3 | 83.23 % |
112
+ | [efficientnet_v2B1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B1_240_fft/efficientnet_v2B1_240_fft_qdq_int8.onnx) | Int8 | 240x240x3 | 82.95 % |
113
+ | [efficientnet_v2B2_260_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft.h5) | Float | 260x260x3 | 84.37 % |
114
+ | [efficientnet_v2B2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2B2_260_fft/efficientnet_v2B2_260_fft_qdq_int8.onnx) | Int8 | 260x260x3 | 84.04 % |
115
+ | [efficientnet_v2S_384_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft.h5) | Float | 384x384x3 | 88.16 % |
116
+ | [efficientnet_v2S_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food-101/efficientnet_v2S_384_fft/efficientnet_v2S_384_fft_qdq_int8.onnx) | Int8 | 384x384x3 | 87.34 % |
117
 
118
 
119
  ### Accuracy with ImageNet
 
125
 
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  | Model | Format | Resolution | Top 1 Accuracy |
127
  |------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|----------------|
128
+ | [efficientnet_v2B0_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224.h5) | Float | 224x224x3 | 73.94 % |
129
+ | [efficientnet_v2B0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B0_224/efficientnet_v2B0_224_qdq_int8.onnx) | Int8 | 224x224x3 | 72.21 % |
130
+ | [efficientnet_v2B1_240](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240.h5) | Float | 240x240x3 | 76.14 % |
131
+ | [efficientnet_v2B1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B1_240/efficientnet_v2B1_240_qdq_int8.onnx) | Int8 | 240x240x3 | 75.5 % |
132
+ | [efficientnet_v2B2_260](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260.h5) | Float | 260x260x3 | 76.58 % |
133
+ | [efficientnet_v2B2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2B2_260/efficientnet_v2B2_260_qdq_int8.onnx) | Int8 | 260x260x3 | 76.26 % |
134
  | [efficientnet_v2S_384](./Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384.h5) | Float | 384x384x3 | 83.52 % |
135
+ | [efficientnet_v2S_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384_qdq_int8.onnx) | Int8 | 384x384x3 | 83.07 % |
136
 
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138
  ## Retraining and Integration in a simple example: