| # EfficientNet |
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| ## **Use case** : `Image classification` |
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| # Model description |
| EfficientNet was initially introduced in this [paper](https://arxiv.org/pdf/1905.11946.pdf). |
| The authors proposed a method that uniformly scales all dimensions depth/width/resolution using a so-called compound coefficient. |
| Using neural architecture search, the authors created the EfficientNet topology and starting from B0, derived a few variants B1...B7 ordered by increasing complexity. |
| Its main building blocks are a mobile inverted bottleneck MBConv (Sandler et al., 2018; Tan et al., 2019) and a squeeze-and-excitation optimization (Hu et al., 2018). |
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| EfficientNet provides state-of-the art accuracy on ImageNet and CIFAR for example while being much smaller and faster |
| than its comparable (ResNet, DenseNet, Inception...). |
| However, for STM32 platforms, B0 is already too large. That's why, we internally derived a custom version tailored for STM32 |
| and modified it to be quantization-friendly (not discussed in the initial paper). This custom model is then quantized in int8 using Tensorflow Lite converter. |
| In the following, the resulting model is called ST EfficientNet LC v1 (LC standing for Low Complexity). |
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| ST EfficientNet LC v1 was obtained after fine-tuning of the original topology. Our goal was to reach around 500 kBytes for RAM and weights. |
| For achieving this, we decided to replace original 'swish' by a simple 'relu6', and search for good expansion factor, depth |
| and width coefficients. Of course, many models could meet the requirement. We selected the one which was better performing on food-101 dataset. |
| We made several attempts to quantize the EfficientNet topology, and discover some issues when quantizing activations. |
| The problem was fixed mainly by adding a clipping lambda layer before the sigmoid. |
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| ## Network information |
| | Network Information | Value | |
| |---------------------|---------------------------------------| |
| | Framework | TensorFlow Lite | |
| | Params | 517540 | |
| | Quantization | int8 | |
| | Paper | https://arxiv.org/pdf/1905.11946.pdf | |
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| The models are quantized using tensorflow lite converter. |
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| ## Network inputs / outputs |
| For an image resolution of NxM and P classes : |
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| | Input Shape | Description | |
| |---------------|----------------------------------------------------------| |
| | (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
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| | Output Shape | Description | |
| |---------------|----------------------------------------------------------| |
| | (1, P) | Per-class confidence for P classes | |
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| ## Recommended platform |
| | Platform | Supported | Recommended | |
| |----------|-----------|---------------| |
| | STM32L0 | [] | [] | |
| | STM32L4 | [] | [] | |
| | STM32U5 | [x] | [] | |
| | STM32H7 | [x] | [x] | |
| | STM32MP1 | [x] | [x] | |
| | STM32MP2 | [x] | [] | |
| | STM32N6 | [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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| * `tfs` stands for "training from scratch", meaning that the model weights were randomly initialized before training. |
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| ### Reference **NPU** memory footprint on food-101 dataset (see Accuracy for details on dataset) |
| |Model | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version | |
| |----------|--------|-------------|------------------|------------------|---------------------|---------------------|----------------------|-------------------------| |
| | [ST EfficientNet LC v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnet/ST_pretrainedmodel_public_dataset/food-101/st_efficientnet_lc_v1_128_tfs/st_efficientnet_lc_v1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6 | 256 | 0 | 579.69 | 10.2.0 | 2.2.0 | |
| | [ST EfficientNet LC v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnet/ST_pretrainedmodel_public_dataset/food-101/st_efficientnet_lc_v1_224_tfs/st_efficientnet_lc_v1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6 | 784.02 | 0 | 586.44 | 10.2.0 | 2.2.0 | |
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| ### Reference **NPU** inference time on food-101 dataset (see Accuracy for details on dataset) |
| | Model | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version | |
| |--------|--------|-------------|------------------|------------------|---------------------|-----------|----------------------|-------------------------| |
| | [ST EfficientNet LC v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnet/ST_pretrainedmodel_public_dataset/food-101/st_efficientnet_lc_v1_128_tfs/st_efficientnet_lc_v1_128_tfs_int8.tflite)| Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 6.88 | 145.34 | 10.2.0 | 2.2.0 | |
| | [ST EfficientNet LC v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/efficientnet/ST_pretrainedmodel_public_dataset/food-101/st_efficientnet_lc_v1_224_tfs/st_efficientnet_lc_v1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 15.76 | 63.45 | 10.2.0 | 2.2.0 | |
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| ### Reference **MCU** memory footprints based on Flowers dataset (see Accuracy for details on dataset) |
| | Model | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version | |
| |---------------------------|--------|--------------|---------|----------------|-------------|---------------|------------|------------|-------------|----------------------| |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | STM32H7 | 430.78 KiB | 58.19 KiB | 505.41 KiB | 158.4 KiB | 488.97 KiB | 663.81 KiB | 10.2.0 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | STM32H7 | 166.78 KiB | 57.86 KiB | 505.41 KiB | 156.74 KiB | 224.64 KiB | 662.15 KiB | 10.2.0 | |
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| ### Reference **MCU** inference time based on Flowers dataset (see Accuracy for details on dataset) |
| | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version | |
| |---------------------------|--------|------------|-------------------|------------------|-----------|---------------------|----------------------| |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 438.33 ms | 10.2.0 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 147.43 ms | 10.2.0 | |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | STM32F769I-DISCO | 1 CPU | 216 MHz | 871.7 ms | 10.2.0 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | STM32F769I-DISCO | 1 CPU | 216 MHz | 259.5 ms | 10.2.0 | |
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| ### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset) |
| | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework | |
| |---------------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------| |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 36.82 ms | 14.72 | 85.28 | 0 | v6.1.0 | OpenVX | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 14.81 ms | 29.68 | 70.32 | 0 | v6.1.0 | OpenVX | |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 137.34 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 45.80 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 195.25 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 65.14 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
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| ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization** |
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| ### Accuracy with Flowers dataset |
| Dataset details: http://download.tensorflow.org/example_images/flower_photos.tgz , License CC - BY 2.0 |
| Number of classes: 5, 3670 files |
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| | Model | Format | Resolution | Top 1 Accuracy (%) | |
| |---------------------------|--------|------------|--------------------| |
| | ST EfficientNet LC v1 tfs | Float | 224x224x3 | 90.19 | |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | 89.92 | |
| | ST EfficientNet LC v1 tfs | Float | 128x128x3 | 87.19 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | 86.78 | |
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| ### Accuracy with Plant dataset |
| Dataset details: https://data.mendeley.com/datasets/tywbtsjrjv/1 , License CC0 1.0 |
| Number of classes: 39, number of files: 55448 |
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| | Model | Format | Resolution | Top 1 Accuracy (%) | |
| |---------------------------|--------|------------|--------------------| |
| | ST EfficientNet LC v1 tfs | Float | 224x224x3 | 99.86 | |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | 99.78 | |
| | ST EfficientNet LC v1 tfs | Float | 128x128x3 | 99.76 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | 99.63 | |
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| ### Accuracy with Food-101 dataset |
| Dataset details: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/, |
| Number of classes: 101, number of files: 101000 |
| |
| | Model | Format | Resolution | Top 1 Accuracy (%) | |
| |---------------------------|--------|------------|--------------------| |
| | ST EfficientNet LC v1 tfs | Float | 224x224x3 | 74.83 | |
| | ST EfficientNet LC v1 tfs | Int8 | 224x224x3 | 74.44 | |
| | ST EfficientNet LC v1 tfs | Float | 128x128x3 | 63.56 | |
| | ST EfficientNet LC v1 tfs | Int8 | 128x128x3 | 63.07 | |
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| |
| ## Retraining and Integration in a simple example: |
| |
| Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) |
| |
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| # References |
| |
| <a id="1">[1]</a> |
| "Tf_flowers : tensorflow datasets," TensorFlow. [Online]. Available: https://www.tensorflow.org/datasets/catalog/tf_flowers. |
| |
| <a id="2">[2]</a> |
| 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 |
| |
| <a id="3">[3]</a> |
| L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014. |
