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---
license: other
license_name: sla0044
license_link: >-
https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/LICENSE.md
pipeline_tag: image-classification
---
# ResNet50 v2
## **Use case** : `Image classification`
# Model description
ResNets family is a well known architecture that uses skip connections to enable stronger gradients in much deeper networks. This variant has 50 layers.
The model is quantized in int8 using tensorflow lite converter.
## Network information
| Network Information | Value |
|-------------------------|-----------------|
| Framework | TensorFlow Lite |
| MParams | 25.6 M |
| Quantization | int8 |
| Provenance | https://www.tensorflow.org/api_docs/python/tf/keras/applications/ResNet50V2 |
| Paper | https://arxiv.org/abs/1603.05027 |
The models are quantized using tensorflow lite converter.
## Network inputs / outputs
For an image resolution of NxM and P classes
| Input Shape | Description |
| ----- | ----------- |
| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 |
| Output Shape | Description |
| ----- | ----------- |
| (1, P) | Per-class confidence for P classes in FLOAT32|
## Recommended platforms
| Platform | Supported | Recommended |
|----------|-----------|-----------|
| STM32L0 |[]|[]|
| STM32L4 |[]|[]|
| STM32U5 |[]|[]|
| STM32H7 |[x]|[]|
| STM32MP1 |[x]|[]|
| STM32MP2 |[x]|[x]|
| STM32N6 |[x]|[x]|
# Performances
## Metrics
- Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
- `tfs` stands for "training from scratch", meaning that the model weights were randomly initialized before training.
- `tl` stands for "transfer learning", meaning that the model backbone weights were initialized from a pre-trained model, then only the last layer was unfrozen during the training.
- `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 | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version |
|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
### 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 |
|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
### Reference **MCU** memory footprint based on Food-101 and ImageNet 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 |
|--------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------|
| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
### Reference **MCU** inference time based on Food-101 and ImageNet dataset (see Accuracy for details on dataset)
| Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
|-------------------|--------|------------|------------------|------------------|-----------|------------------|-----------------------|
| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/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 |
### Accuracy with Food-101 dataset
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
| Model | Format | Resolution | Top 1 Accuracy |
|-------|--------|------------|----------------|
| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft.h5) | Float | 224x224x3 | 71.53 % |
| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food-101/resnet50_v2_224_fft/resnet50_v2_224_fft_int8.tflite) | Int8 | 224x224x3 | 70.07 % |
### Accuracy with ImageNet dataset
Dataset details: [link](https://www.image-net.org), License: BSD-3-Clause, 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 50000 labelled images of the validation set.
|model | Format | Resolution | Top 1 Accuracy |
|---------|--------|------------|----------------|
| [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224.h5) | Float | 224x224x3 | 66.38 % |
| [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/ImageNet/resnet50_v2_224/resnet50_v2_224_int8.tflite) | Int8 | 224x224x3 | 65.99 % |
## Retraining and Integration in a simple example:
Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
# References
<a id="1">[1]</a>
L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014. |