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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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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ ---
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+ # EfficientNet v2
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+
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+ ## **Use case** : `Image classification`
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+
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+ # Model description
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+
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+
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+ EfficientNet v2 family is one of the best topology for image classification. It has been obtained through neural architecture search with a special care given to training time
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+ and number of parameters reduction.
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+
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+ This family of networks comprises various subtypes: B0 (224x224), B1 (240x240), B2 (260x260), B3 (300x300), S (384x384) ranked by depth and width increasing order.
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+ There are also M, L, XL variants but too large to be executed efficiently on STM32N6.
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+
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+ All these networks are already available on https://www.tensorflow.org/api_docs/python/tf/keras/applications/ pre-trained on ImageNet.
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+
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+
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+ ## Network information
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+
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+
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+ | Network Information | Value |
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+ |---------------------|----------------------------------------------------------------------------------|
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+ | Framework | TensorFlow Lite/ONNX quantizer |
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+ | MParams type=B0 | 7.1 M |
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+ | Quantization | int8 |
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+ | Provenance | https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet_v2 |
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+ | Paper | https://arxiv.org/pdf/2104.00298 |
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+
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+ The models are quantized using tensorflow lite converter or ONNX quantizer.
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+
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+
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+ ## Network inputs / outputs
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+
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+
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+ For an image resolution of NxM and P classes
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+
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+ | Input Shape | Description |
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+ |---------------|---------------------------------------------------------------------|
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+ | (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 for tflite |
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+ | (1, 3, N, M) | Single NxM RGB image with INT8 values between -128 and 127 for ONNX |
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+
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+ | Output Shape | Description |
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+ | ----- |----------------------------------------------------------|
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+ | (1, P) | Per-class confidence for P classes in FLOAT32 for tflite |
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+ | (1, P) | Per-class confidence for P classes in FLOAT32 for ONNX |
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+
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+
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+ ## Recommended platforms
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+
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+
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+ | Platform | Supported | Recommended |
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+ |-----------|-----------|-------------|
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+ | STM32L0 |[]| [] |
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+ | STM32L4 |[]| [] |
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+ | STM32U5 |[]| [] |
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+ | STM32H7 |[]| [] |
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+ | STM32MP1 |[x]| [x] |
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+ | STM32MP2 |[x]| [x] |
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+ | STM32N6 |[x]| [x] |
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+
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+
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+ # Performances
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+
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+ ## Metrics
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+
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+ Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
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+
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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 (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
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+ |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+
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+
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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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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+
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+ * 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.
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+ ### Accuracy with Food-101 dataset
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+
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+ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) , License [-](), Quotation[[3]](#3) , Number of classes: 101 , Number of images: 101 000
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+
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+ | Model | Format | Resolution | Top 1 Accuracy |
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+ |--------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|----------------|
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+
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+
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+ ### Accuracy with ImageNet
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+
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+ Dataset details: [link](https://www.image-net.org), License: BSD-3-Clause, 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 10000 labelled images of the validation set.
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+
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+ | Model | Format | Resolution | Top 1 Accuracy |
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+ |------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|----------------|
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [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 % |
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+ | [efficientnet_v2S_384](./Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384.h5) | Float | 384x384x3 | 83.52 % |
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+ | [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 % |
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+
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+
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+ ## Retraining and Integration in a simple example:
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+
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+ Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
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+
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+
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+ # References
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+
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+ <a id="1">[1]</a>
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+ "Tf_flowers : tensorflow datasets," TensorFlow. [Online]. Available: https://www.tensorflow.org/datasets/catalog/tf_flowers.
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+
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+ <a id="2">[2]</a>
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+ J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), "Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network", Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1
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+
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+ <a id="3">[3]</a>
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+ L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014.
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+
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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.
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+