resnet50v2 / README.md

Release AI-ModelZoo-4.0.0

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	---
	license: other
	license_name: sla0044
	license_link: >-
	https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/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. A mixed precision version is also provided using onnx-runtime and our own quantization scripts.

	## 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 food101 and imagenet dataset (see Accuracy for details on dataset)
	\|Model \| Dataset \| Format \| Resolution \| Series \| Internal RAM \| External RAM \| Weights Flash \| STEdgeAI Core version \|
	\|----------\|------------------\|--------\|-------------\|------------------\|--------------\|--------------\|----------------------\|-------------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32N6 \| 2308.06 \| 3136 \| 23833.67 \| 3.0.0 \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_qdq_w4_91.4%_w8_8.6%_a8_100%_acc_80.17.onnx) \| food101 \| Int8/Int4 \| 224x224x3 \| STM32N6 \| 2308.06 \| 2352 \| 13268.39 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32N6 \| 2308.06 \| 3136.0 \| 25633.61 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_qdq_w4_35.98%_w8_64.02%_a8_100%_acc_67.45.onnx) \| imagenet \| Int8/Int4 \| 224x224x3 \| STM32N6 \| 2308.06 \| 2352 \| 21154.53 \| 3.0.0 \|

	### Reference NPU inference time on food101 and imagenet dataset (see Accuracy for details on dataset)
	\| Model \| Dataset \| Format \| Resolution \| Board \| Execution Engine \| Inference time (ms) \| Inf / sec \| STEdgeAI Core version \|
	\|--------\|------------------\|--------\|-------------\|------------------\|------------------\|---------------------\|-----------\|-------------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 238.49 \| 4.19 \| 3.0.0 \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_qdq_w4_91.4%_w8_8.6%_a8_100%_acc_80.17.onnx) \| food101 \| Int8/Int4 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 267.33 \| 3.74 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 243.04 \| 4.11 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_qdq_w4_35.98%_w8_64.02%_a8_100%_acc_67.45.onnx) \| imagenet \| Int8/Int4 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 286.06 \| 3.5 \| 3.0.0 \|


	### Reference MCU memory footprint based on Food-101 and imagenet dataset (see Accuracy for details on dataset)

	\| Model \| Dataset \| Format \| Resolution \| Series \| Activation RAM \| Runtime RAM \| Weights Flash \| Code Flash \| Total RAM \| Total Flash \| STEdgeAI Core version \|
	\|-----------\|---------------------------------------------------------------------------------------------------------------------------\|--------\|------------\|---------\|----------------\|-------------\|---------------\|------------\|-------------\|-----------------\|-----------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32H7 \| 1816.2 KiB \| 14.56 KiB \| 23240.96 KiB \| 169.12 KiB \| 1830.76 KiB \| 23410.08 KiB \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32H7 \| 2142.07 KiB \| 41.03 KiB \| 25042.47 KiB \| 225.32 KiB \| 2183.1 KiB \| 25267.79 KiB \| 3.0.0 \|


	### Reference MCU inference time based on Food-101 and imagenet dataset (see Accuracy for details on dataset)


	\| Model \| Dataset \| Format \| Resolution \| Board \| Execution Engine \| Frequency \| Inference time (ms) \| STEdgeAI Core version \|
	\|-------------------\|-----\|---\|------------\|------------------\|------------------\|-----------\|---------------------\|-----------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32H747I-DISCO \| 1 CPU \| 400 MHz \| 11314.82 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32H747I-DISCO \| 1 CPU \| 400 MHz \| 11370.07 \| 3.0.0 \|



	### Accuracy with Food-101 dataset

	Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/), 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/food101/resnet50v2_224_fft/resnet50v2_224_fft.keras) \| Float \| 224x224x3 \| 82.2 % \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| Int8 \| 224x224x3 \| 81.03 % \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_qdq_w4_91.4%_w8_8.6%_a8_100%_acc_80.17.onnx) \| Int8/Int4 \| 224x224x3 \| 80.17 % \|

	### Accuracy with imagenet dataset

	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 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/resnet50v2_224/resnet50v2_224.keras) \| Float \| 224x224x3 \| 68.73 % \|
	\| [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| Int8 \| 224x224x3 \| 67.99 % \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_qdq_w4_35.98%_w8_64.02%_a8_100%_acc_67.45.onnx) \| Int8/Int4 \| 224x224x3 \| 67.45 % \|


	## 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.

	---
	license: other
	license_name: sla0044
	license_link: >-
	https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/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. A mixed precision version is also provided using onnx-runtime and our own quantization scripts.

	## 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 food101 and imagenet dataset (see Accuracy for details on dataset)
	\|Model \| Dataset \| Format \| Resolution \| Series \| Internal RAM \| External RAM \| Weights Flash \| STEdgeAI Core version \|
	\|----------\|------------------\|--------\|-------------\|------------------\|--------------\|--------------\|----------------------\|-------------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32N6 \| 2308.06 \| 3136 \| 23833.67 \| 3.0.0 \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_qdq_w4_91.4%_w8_8.6%_a8_100%_acc_80.17.onnx) \| food101 \| Int8/Int4 \| 224x224x3 \| STM32N6 \| 2308.06 \| 2352 \| 13268.39 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32N6 \| 2308.06 \| 3136.0 \| 25633.61 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_qdq_w4_35.98%_w8_64.02%_a8_100%_acc_67.45.onnx) \| imagenet \| Int8/Int4 \| 224x224x3 \| STM32N6 \| 2308.06 \| 2352 \| 21154.53 \| 3.0.0 \|

	### Reference NPU inference time on food101 and imagenet dataset (see Accuracy for details on dataset)
	\| Model \| Dataset \| Format \| Resolution \| Board \| Execution Engine \| Inference time (ms) \| Inf / sec \| STEdgeAI Core version \|
	\|--------\|------------------\|--------\|-------------\|------------------\|------------------\|---------------------\|-----------\|-------------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 238.49 \| 4.19 \| 3.0.0 \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_qdq_w4_91.4%_w8_8.6%_a8_100%_acc_80.17.onnx) \| food101 \| Int8/Int4 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 267.33 \| 3.74 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 243.04 \| 4.11 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_qdq_w4_35.98%_w8_64.02%_a8_100%_acc_67.45.onnx) \| imagenet \| Int8/Int4 \| 224x224x3 \| STM32N6570-DK \| NPU/MCU \| 286.06 \| 3.5 \| 3.0.0 \|


	### Reference MCU memory footprint based on Food-101 and imagenet dataset (see Accuracy for details on dataset)

	\| Model \| Dataset \| Format \| Resolution \| Series \| Activation RAM \| Runtime RAM \| Weights Flash \| Code Flash \| Total RAM \| Total Flash \| STEdgeAI Core version \|
	\|-----------\|---------------------------------------------------------------------------------------------------------------------------\|--------\|------------\|---------\|----------------\|-------------\|---------------\|------------\|-------------\|-----------------\|-----------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32H7 \| 1816.2 KiB \| 14.56 KiB \| 23240.96 KiB \| 169.12 KiB \| 1830.76 KiB \| 23410.08 KiB \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32H7 \| 2142.07 KiB \| 41.03 KiB \| 25042.47 KiB \| 225.32 KiB \| 2183.1 KiB \| 25267.79 KiB \| 3.0.0 \|


	### Reference MCU inference time based on Food-101 and imagenet dataset (see Accuracy for details on dataset)


	\| Model \| Dataset \| Format \| Resolution \| Board \| Execution Engine \| Frequency \| Inference time (ms) \| STEdgeAI Core version \|
	\|-------------------\|-----\|---\|------------\|------------------\|------------------\|-----------\|---------------------\|-----------------------\|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| food101 \| Int8 \| 224x224x3 \| STM32H747I-DISCO \| 1 CPU \| 400 MHz \| 11314.82 \| 3.0.0 \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| imagenet \| Int8 \| 224x224x3 \| STM32H747I-DISCO \| 1 CPU \| 400 MHz \| 11370.07 \| 3.0.0 \|



	### Accuracy with Food-101 dataset

	Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/), 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/food101/resnet50v2_224_fft/resnet50v2_224_fft.keras) \| Float \| 224x224x3 \| 82.2 % \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_int8.tflite) \| Int8 \| 224x224x3 \| 81.03 % \|
	\| [ResNet50 v2 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/ST_pretrainedmodel_public_dataset/food101/resnet50v2_224_fft/resnet50v2_224_fft_qdq_w4_91.4%_w8_8.6%_a8_100%_acc_80.17.onnx) \| Int8/Int4 \| 224x224x3 \| 80.17 % \|

	### Accuracy with imagenet dataset

	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 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/resnet50v2_224/resnet50v2_224.keras) \| Float \| 224x224x3 \| 68.73 % \|
	\| [ResNet50 v2 ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_int8.tflite) \| Int8 \| 224x224x3 \| 67.99 % \|
	\| [ResNet50 v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet50v2/Public_pretrainedmodel_public_dataset/imagenet/resnet50v2_224/resnet50v2_224_qdq_w4_35.98%_w8_64.02%_a8_100%_acc_67.45.onnx) \| Int8/Int4 \| 224x224x3 \| 67.45 % \|


	## 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.