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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/raw/refs/heads/main/image_classification/LICENSE.md |
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pipeline_tag: image-classification |
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--- |
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# EfficientNet v2 |
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## **Use case** : `Image classification` |
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# Model description |
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EfficientNet v2 family is one of the best topologies for image classification. It has been obtained through neural architecture search with a special care given to training time and number of parameters reduction. |
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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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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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## Network information |
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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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The models are quantized using tensorflow lite converter or ONNX quantizer. |
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## Network inputs / outputs |
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For an image resolution of NxM and P classes |
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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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| 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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## Recommended platforms |
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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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# Performances |
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## Metrics |
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* Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option. |
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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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### Reference **NPU** memory footprint on food101 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) | STEdgeAI Core version | |
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|-----------|---------------|----------|------------|-----------|--------------------|--------------------|---------------------|-----------------------| |
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| [efficientnetv2b0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b0_224_fft/efficientnetv2b0_224_fft_qdq_int8.onnx) | food101 | Int8 | 224x224x3 | STM32N6 | 1911.56 |0.0| 6839.39 | 3.0.0 | |
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| [efficientnetv2b0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b0_224_fft/efficientnetv2b0_224_fft_qdq_w4_90.1%_w8_9.9%_a8_100%_acc_84.47.onnx) | food101 | Int8/Int4 | 224x224x3 | STM32N6 | 1911.56 |0.0| 4237.52 | 3.0.0 | |
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| [efficientnetv2b1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b1_240_fft/efficientnetv2b1_240_fft_qdq_int8.onnx) | food101 | Int8 | 240x240x3 | STM32N6 | 2604.03 |0.0| 8089.27 | 3.0.0 | |
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| [efficientnetv2b1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b1_240_fft/efficientnetv2b1_240_fft_qdq_w4_91.8%_w8_8.2%_a8_100%_acc_85.71.onnx) | food101 | Int8/Int4 | 240x240x3 | STM32N6 | 2604.03 |0.0| 4995.39 | 3.0.0 | |
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| [efficientnetv2b2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b2_260_fft/efficientnetv2b2_260_fft_qdq_int8.onnx) | food101 | Int8 | 260x260x3 | STM32N6 | 2712.19 |528.12| 10328.52 | 3.0.0 | |
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| [efficientnetv2b2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b2_260_fft/efficientnetv2b2_260_fft_qdq_w4_81.26%_w8_18.74%_a8_100%_acc_87.24.onnx) | food101 | Int8/Int4 | 260x260x3 | STM32N6 | 2712.19 |528.12| 6865.39 | 3.0.0 | |
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| [efficientnetv2s_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2s_384_fft/efficientnetv2s_384_fft_qdq_int8.onnx) | food101 | Int8 | 384x384x3 | STM32N6 | 2757 | 3456 | 24262.34 | 3.0.0 | |
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| [efficientnetv2s_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2s_384_fft/efficientnetv2s_384_fft_qdq_w4_95.95%_w8_4.05%_a8_100%_acc_89.87.onnx) | food101 | Int8/Int4 | 384x384x3 | STM32N6 | 2757 | 3456 | 14836.94 | 3.0.0 | |
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| [efficientnetv2b0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b0_224/efficientnetv2b0_224_qdq_int8.onnx) | imagenet | Int8 | 224x224x3 | STM32N6 | 1911.56 | 0.0 | 7967.05 | 3.0.0 | |
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| [efficientnetv2b0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b0_224/efficientnetv2b0_224_qdq_w4_65.43%_w8_34.57%_a8_100%_acc_73.38.onnx) | imagenet | Int8/Int4 | 224x224x3 | STM32N6 | 1911.56 | 0.0 | 5710.05 | 3.0.0 | |
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| [efficientnetv2b1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b1_240/efficientnetv2b1_240_qdq_int8.onnx) | imagenet | Int8 | 240x240x3 | STM32N6 | 2604.03 | 0.0 | 9216.92 | 3.0.0 | |
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| [efficientnetv2b1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b1_240/efficientnetv2b1_240_qdq_w4_73.1%_w8_26.9%_a8_100%_acc_73.92.onnx) | imagenet | Int8/Int4 | 240x240x3 | STM32N6 | 2604.03 | 0.0 | 6342.67 | 3.0.0 | |
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| [efficientnetv2b2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b2_260/efficientnetv2b2_260_qdq_int8.onnx) | imagenet | Int8 | 260x260x3 | STM32N6 | 2712.19 | 528.12 | 11568.55 | 3.0.0 | |
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| [efficientnetv2b2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b2_260/efficientnetv2b2_260_qdq_w4_67.53%_w8_32.47%_a8_100%_acc_74.71.onnx) | imagenet | Int8/Int4 | 260x260x3 | STM32N6 | 2712.19 | 528.12 | 8273.17 | 3.0.0 | |
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| [efficientnetv2b3_300 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b3_300/efficientnetv2b3_300_qdq_int8.onnx) | imagenet | Int8 | 300x300x3 | STM32N6 | 2574.47 | 1757.81 | 16510.05 | 3.0.0 | |
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| [efficientnetv2b3_300 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b3_300/efficientnetv2b3_300_qdq_w4_88.31%_w8_11.69%_a8_100%_acc_78.11.onnx) | imagenet | Int8/Int4 | 300x300x3 | STM32N6 | 2574.47 | 1757.81 | 10376.74 | 3.0.0 | |
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| [efficientnetv2s_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2s_384/efficientnetv2s_384_qdq_int8.onnx) | imagenet | Int8 | 384x384x3 | STM32N6 | 2800 | 2592 | 25390 | 3.0.0 | |
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| [efficientnetv2s_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2s_384/efficientnetv2s_384_qdq_w4_95.63%_w8_4.37%_a8_100%_acc_82.25.onnx) | imagenet | Int8/Int4 | 384x384x3 | STM32N6 | 2800 | 2592 | 15458.97 | 3.0.0 | |
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### Reference **NPU** inference time on food101 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 | STEdgeAI Core version | |
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|--------|------------------|--------|-------------|------------------|------------------|---------------------|-----------|------------------------| |
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| [efficientnetv2b0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b0_224_fft/efficientnetv2b0_224_fft_qdq_int8.onnx) | food101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 62.48 | 16 | 3.0.0 | |
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| [efficientnetv2b0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b0_224_fft/efficientnetv2b0_224_fft_qdq_w4_90.1%_w8_9.9%_a8_100%_acc_84.47.onnx) | food101 | Int8/Int4 | 224x224x3 | STM32N6570-DK | NPU/MCU | 57.05 | 17.53 | 3.0.0 | |
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| [efficientnetv2b1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b1_240_fft/efficientnetv2b1_240_fft_qdq_int8.onnx) | food101 | Int8 | 240x240x3 | STM32N6570-DK | NPU/MCU | 86.55 | 11.55 | 3.0.0 | |
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| [efficientnetv2b1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b1_240_fft/efficientnetv2b1_240_fft_qdq_w4_91.8%_w8_8.2%_a8_100%_acc_85.71.onnx) | food101 | Int8/Int4 | 240x240x3 | STM32N6570-DK | NPU/MCU | 80.5 | 12.42 | 3.0.0 | |
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| [efficientnetv2b2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b2_260_fft/efficientnetv2b2_260_fft_qdq_int8.onnx) | food101 | Int8 | 260x260x3 | STM32N6570-DK | NPU/MCU | 147.21 | 6.79 | 3.0.0 | |
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| [efficientnetv2b2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b2_260_fft/efficientnetv2b2_260_fft_qdq_w4_81.26%_w8_18.74%_a8_100%_acc_87.24.onnx) | food101 | Int8/Int4 | 260x260x3 | STM32N6570-DK | NPU/MCU | 140.38 | 7.12 | 3.0.0 | |
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| [efficientnetv2s_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2s_384_fft/efficientnetv2s_384_fft_qdq_int8.onnx) | food101 | Int8 | 384x384x3 | STM32N6570-DK | NPU/MCU | 1089.83 | 0.92 | 3.0.0 | |
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| [efficientnetv2s_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2s_384_fft/efficientnetv2s_384_fft_qdq_w4_95.95%_w8_4.05%_a8_100%_acc_89.87.onnx) | food101 | Int8/Int4 | 384x384x3 | STM32N6570-DK | NPU/MCU | 1078.35 | 0.93 | 3.0.0 | |
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| [efficientnetv2b0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b0_224/efficientnetv2b0_224_qdq_int8.onnx) | imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 65.44 | 15.28 | 3.0.0 | |
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| [efficientnetv2b0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b0_224/efficientnetv2b0_224_qdq_w4_65.43%_w8_34.57%_a8_100%_acc_73.38.onnx) | imagenet | Int8/Int4 | 224x224x3 | STM32N6570-DK | NPU/MCU | 59.54 | 16.80 | 3.0.0 | |
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| [efficientnetv2b1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b1_240/efficientnetv2b1_240_qdq_int8.onnx) | imagenet | Int8 | 240x240x3 | STM32N6570-DK | NPU/MCU | 89.71 | 11.15 | 3.0.0 | |
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| [efficientnetv2b1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b1_240/efficientnetv2b1_240_qdq_w4_73.1%_w8_26.9%_a8_100%_acc_73.92.onnx) | imagenet | Int8/Int4 | 240x240x3 | STM32N6570-DK | NPU/MCU | 83.2 | 12.02 | 3.0.0 | |
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| [efficientnetv2b2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b2_260/efficientnetv2b2_260_qdq_int8.onnx) | imagenet | Int8 | 260x260x3 | STM32N6570-DK | NPU/MCU | 150.04 | 6.66 | 3.0.0 | |
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| [efficientnetv2b2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b2_260/efficientnetv2b2_260_qdq_w4_67.53%_w8_32.47%_a8_100%_acc_74.71.onnx) | imagenet | Int8/Int4 | 260x260x3 | STM32N6570-DK | NPU/MCU | 141.94 | 7.05 | 3.0.0 | |
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| [efficientnetv2b3_300 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b3_300/efficientnetv2b3_300_qdq_int8.onnx) | imagenet | Int8 | 300x300x3 | STM32N6570-DK | NPU/MCU | 224.03 | 4.46 | 3.0.0 | |
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| [efficientnetv2b3_300 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b3_300/efficientnetv2b3_300_qdq_w4_88.31%_w8_11.69%_a8_100%_acc_78.11.onnx) | imagenet | Int8/Int4 | 300x300x3 | STM32N6570-DK | NPU/MCU | 219.31 | 4.56 | 3.0.0 | |
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| [efficientnetv2s_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2s_384/efficientnetv2s_384_qdq_int8.onnx) | imagenet | Int8 | 384x384x3 | STM32N6570-DK | NPU/MCU | 839.14 | 1.19 | 3.0.0 | |
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| [efficientnetv2s_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2s_384/efficientnetv2s_384_qdq_w4_95.63%_w8_4.37%_a8_100%_acc_82.25.onnx) | imagenet | Int8/Int4 | 384x384x3 | STM32N6570-DK | NPU/MCU | 826.23 | 1.21 | 3.0.0 | |
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### Accuracy with Food-101 dataset |
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Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/), Quotation[[3]](#3) , Number of classes: 101 , Number of images: 101 000 |
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| Model | Format | Resolution | Top 1 Accuracy | |
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|--------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|----------------| |
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| [efficientnetv2b0_224_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b0_224_fft/efficientnetv2b0_224_fft.keras) | Float | 224x224x3 | 86.59 % | |
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| [efficientnetv2b0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b0_224_fft/efficientnetv2b0_224_fft_qdq_int8.onnx) | Int8 | 224x224x3 | 85.98 % | |
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| [efficientnetv2b0_224_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b0_224_fft/efficientnetv2b0_224_fft_qdq_w4_90.1%_w8_9.9%_a8_100%_acc_84.47.onnx)| Int8/Int4 | 224x224x3 | 84.47 % | |
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| [efficientnetv2b1_240_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b1_240_fft/efficientnetv2b1_240_fft.keras) | Float | 240x240x3 | 87.71 % | |
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| [efficientnetv2b1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b1_240_fft/efficientnetv2b1_240_fft_qdq_int8.onnx) | Int8 | 240x240x3 | 87.09 % | |
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| [efficientnetv2b1_240_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b1_240_fft/efficientnetv2b1_240_fft_qdq_w4_91.8%_w8_8.2%_a8_100%_acc_85.71.onnx) | Int8/Int4 | 240x240x3 | 85.71 % | |
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| [efficientnetv2b2_260_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b2_260_fft/efficientnetv2b2_260_fft.keras) | Float | 260x260x3 | 88.67 % | |
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| [efficientnetv2b2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b2_260_fft/efficientnetv2b2_260_fft_qdq_int8.onnx) | Int8 | 260x260x3 | 88.44 % | |
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| [efficientnetv2b2_260_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2b2_260_fft/efficientnetv2b2_260_fft_qdq_w4_81.26%_w8_18.74%_a8_100%_acc_87.24.onnx) | Int8/Int4 | 260x260x3 | 87.24 % | |
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| [efficientnetv2s_384_fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2s_384_fft/efficientnetv2s_384_fft.keras) | Float | 384x384x3 | 91.69 % | |
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| [efficientnetv2s_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2s_384_fft/efficientnetv2s_384_fft_qdq_int8.onnx) | Int8 | 384x384x3 | 91.34 % | |
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| [efficientnetv2s_384_fft onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/ST_pretrainedmodel_public_dataset/food101/efficientnetv2s_384_fft/efficientnetv2s_384_fft_qdq_w4_95.95%_w8_4.05%_a8_100%_acc_89.87.onnx) | Int8/Int4 | 384x384x3 | 89.87 % | |
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### Accuracy with imagenet |
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Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4). |
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Number of classes: 1000. |
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To perform the quantization, we calibrated the activations with a random subset of the training set. |
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For the sake of simplicity, the accuracy reported here was estimated on the 10000 labelled images of the validation set. |
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| Model | Format | Resolution | Top 1 Accuracy | |
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|------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|----------------| |
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| [efficientnetv2b0_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b0_224/efficientnetv2b0_224.keras) | Float | 224x224x3 | 75.18 % | |
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| [efficientnetv2b0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b0_224/efficientnetv2b0_224_qdq_int8.onnx) | Int8 | 224x224x3 | 73.75 % | |
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| [efficientnetv2b0_224 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b0_224/efficientnetv2b0_224_qdq_w4_65.43%_w8_34.57%_a8_100%_acc_73.38.onnx) | Int8/Int4 | 224x224x3 | 73.38 % | |
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| [efficientnetv2b1_240](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b1_240/efficientnetv2b1_240.keras) | Float | 240x240x3 | 76.14 % | |
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| [efficientnetv2b1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b1_240/efficientnetv2b1_240_qdq_int8.onnx) | Int8 | 240x240x3 | 75.19 % | |
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| [efficientnetv2b1_240 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b1_240/efficientnetv2b1_240_qdq_w4_73.1%_w8_26.9%_a8_100%_acc_73.92.onnx) | Int8/Int4 | 240x240x3 | 73.92 % | |
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| [efficientnetv2b2_260](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b2_260/efficientnetv2b2_260.keras) | Float | 260x260x3 | 76.58 % | |
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| [efficientnetv2b2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b2_260/efficientnetv2b2_260_qdq_int8.onnx) | Int8 | 260x260x3 | 76.14 % | |
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|[efficientnetv2b2_260 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b2_260/efficientnetv2b2_260_qdq_w4_67.53%_w8_32.47%_a8_100%_acc_74.71.onnx) | Int8/Int4 | 260x260x3 | 74.71 % | |
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| [efficientnetv2b3_300](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b3_300/efficientnetv2b3_300.keras) | Float | 300x300x3 | 79.18 % | |
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| [efficientnetv2b3_300 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b3_300/efficientnetv2b3_300_qdq_int8.onnx) | Int8 | 300x300x3 | 79.05 % | |
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| [efficientnetv2b3_300 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2b3_300/efficientnetv2b3_300_qdq_w4_88.31%_w8_11.69%_a8_100%_acc_78.11.onnx) | Int8/Int4 | 300x300x3 | 78.11 % | |
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| [efficientnetv2s_384](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2s_384/efficientnetv2s_384.keras) | Float | 384x384x3 | 83.52 % | |
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| [efficientnetv2s_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2s_384/efficientnetv2s_384_qdq_int8.onnx) | Int8 | 384x384x3 | 83.07 % | |
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| [efficientnetv2s_384 onnx](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/imagenet/efficientnetv2s_384/efficientnetv2s_384_qdq_w4_95.63%_w8_4.37%_a8_100%_acc_82.25.onnx) | Int8/Int4 | 384x384x3 | 82.25 % | |
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## Retraining and Integration in a simple example: |
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Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) |
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# References |
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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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<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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<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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<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. |
