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resnet50v2

Image Classification
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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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+ pipeline_tag: image-classification
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+ ---
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+ # ResNet50 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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+ ResNets family is a well known architecture that uses skip connections to enable stronger gradients in much deeper networks. This variant has 50 layers.
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+ The model is quantized in int8 using tensorflow lite converter.
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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 |
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+ | MParams | 25.6 M |
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+ | Quantization | int8 |
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+ | Provenance | https://www.tensorflow.org/api_docs/python/tf/keras/applications/ResNet50V2 |
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+ | Paper | https://arxiv.org/abs/1603.05027 |
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+
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+ The models are quantized using tensorflow lite converter.
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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 |
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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|
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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 |[x]|[]|
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+ | STM32MP1 |[x]|[]|
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+ | STM32MP2 |[x]|[x]|
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+ | STM32N6 |[x]|[x]|
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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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+
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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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+ ### 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 | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version |
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+ |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6 | | | | 10.0.0 | 2.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6 | | | | 10.0.0 | 2.0.0 |
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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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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | food-101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | | | 10.0.0 | 2.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | ImageNet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | | | 10.0.0 | 2.0.0 |
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+
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+
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+ ### Reference **MCU** memory footprint based on Food-101 and ImageNet dataset (see Accuracy for details on dataset)
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+ | Model | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
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+ |--------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------|
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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 2142.07 KiB | 41.02 KiB | 23240.96 KiB | 226.05 KiB | 2183.09 KiB | 23467.01 KiB | 10.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 2142.07 KiB | 41.02 KiB | 25042.47 KiB | 226.05 KiB | 2183.09 KiB | 25268.52 KiB | 10.0.0 |
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+
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+ ### Reference **MCU** inference time based on Food-101 and ImageNet dataset (see Accuracy for details on dataset)
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+ | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
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+ |-------------------|--------|------------|------------------|------------------|-----------|------------------|-----------------------|
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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 11354.82 ms | 10.0.0 |
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+ | [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 11368.81 ms | 10.0.0 |
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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/) , License [-](), Quotation[[1]](#1) , 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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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft.h5) | Float | 224x224x3 | 71.53 % |
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+ | [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | 70.07 % |
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+ ### Accuracy with ImageNet dataset
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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 50000 labelled images of the validation set.
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+ |model | Format | Resolution | Top 1 Accuracy |
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+ |---------|--------|------------|----------------|
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+ | [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224.h5) | Float | 224x224x3 | 66.38 % |
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+ | [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | 65.99 % |
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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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+
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+ <a id="1">[1]</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.