Update ST Model Zoo

README.md

@@ -1,9 +1,3 @@
----
-license: other
-license_name: sla0044
-license_link: >-
-  https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/LICENSE.md
----
 # EfficientNet v2
 
 ## **Use case** : `Image classification`
@@ -11,7 +5,7 @@ license_link: >-
 # Model description
 
 
-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 
+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.
 
 This family of networks comprises various subtypes: B0 (224x224), B1 (240x240), B2 (260x260), B3 (300x300), S (384x384) ranked by depth and width increasing order.
@@ -69,40 +63,37 @@ For an image resolution of NxM and P classes
 ## Metrics
 
 * Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
-  
 * `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.
 
-
-
 ### Reference **NPU** memory footprint on food-101 and ImageNet dataset (see Accuracy for details on dataset)
 |Model      | Dataset       | Format   | Resolution | Series    | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
-|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
-| [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   |
-| [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   |
-| [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   |
-| [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 |
-| [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 |
-| [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 |
-| [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 |
-| [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 |
+|----------|------------------|--------|-------------|------------------|------------------|---------------------|---------------------|----------------------|-------------------------|
+| [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| 7552.02             |       10.2.0        |     2.2.0   |
+| [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| 8332.27             |       10.2.0        |     2.2.0   |
+| [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| 10525.95            |       10.2.0        |     2.2.0   |
+| [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  | STM32N6   | 2700 | 6912 | 24451.31            | 10.2.0 | 2.2.0 |
+| [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 | 8179.67             | 10.2.0 | 2.2.0 |
+| [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 | 9459.92             | 10.2.0 | 2.2.0 |
+| [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 | 11765.99           | 10.2.0 | 2.2.0 |
+| [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 | 25579.03           | 10.2.0 | 2.2.0 |
 
 
 ### Reference **NPU**  inference time on food-101 and ImageNet dataset (see Accuracy for details on dataset)
-| Model  | Dataset          | Format | Resolution  | Board            | Execution Engine | Inference time (ms) | Inf / sec   | STM32Cube.AI version  |  STEdgeAI Core version |
-|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
-| [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   |
-| [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   |
-| [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   |
-| [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   |
-| [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   |
-| [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   |
-| [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   |
-| [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   |
+| Model  | Dataset          | Format | Resolution  | Board            | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version  |  STEdgeAI Core version |
+|--------|------------------|--------|-------------|------------------|------------------|---------------------|-----------|----------------------|-------------------------|
+| [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      | 52.05               | 19.21     |       10.2.0        |     2.2.0   |
+| [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      | 70.91               | 14.1      |       10.2.0        |     2.2.0   |
+| [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      | 142.62              | 7.01      |       10.2.0        |     2.2.0   |
+| [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 | 816.34              | 1.22      |       10.2.0        |     2.2.0   |
+| [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      | 55.27               | 18.09     |       10.2.0        |     2.2.0   |
+| [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      | 74.48             | 13.34     |       10.2.0        |     2.2.0   |
+| [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      | 145.27             | 6.88      |       10.2.0        |     2.2.0   |
+| [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      | 785.01             | 1.27     |       10.2.0        |     2.2.0   |
 
 * 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.
 ### Accuracy with Food-101 dataset
 
-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
+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
 
 | Model                                                                                                                                            | Format | Resolution | Top 1 Accuracy |
 |--------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|----------------|
@@ -110,7 +101,7 @@ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-1
 | [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 %        |
 | [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 %        |
 | [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 %        |
-| [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 %        |
+| [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 | 84.35 %        |
 | [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 %        |
 | [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 %        |
 | [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 %        |
@@ -118,7 +109,7 @@ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-1
 
 ### Accuracy with ImageNet
 
-Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4)
+Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4).
 Number of classes: 1000.
 To perform the quantization, we calibrated the activations with a random subset of the training set.
 For the sake of simplicity, the accuracy reported here was estimated on the 10000 labelled images of the validation set.
@@ -131,7 +122,7 @@ For the sake of simplicity, the accuracy reported here was estimated on the 1000
 | [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 %        |
 | [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 %        |
 | [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 %        |
-| [efficientnet_v2S_384](./Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384.h5)                    | Float  | 384x384x3  | 83.52 %        |
+| [efficientnet_v2S_384](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnetv2/Public_pretrainedmodel_public_dataset/ImageNet/efficientnet_v2S_384/efficientnet_v2S_384.h5)                    | Float  | 384x384x3  | 83.52 %        |
 | [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 %        |
 
 
@@ -153,5 +144,4 @@ L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative C
 
 <a id="4">[4]</a>
 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.
-(* = equal contribution) ImageNet Large Scale Visual Recognition Challenge.
-
+(* = equal contribution) ImageNet Large Scale Visual Recognition Challenge.