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ResNet v1

Use case : Image classification

Model description

ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ResNet models consist of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. The models below have 8 and 32 layers with ResNet v1 architecture. (source: https://keras.io/api/applications/resnet/) The model is quantized in int8 using tensorflow lite converter.

In addition, we introduce a new model family inspired from ResNet v1 which takes benefit from hybrid quantization. Later on, they are named as ST ResNet 8 Hybrid v1 and ST ResNet 8 Hybrid v2. By hybrid quantization, we mean that whenever it is possible, some network layers are quantized for weights and/or activations on less than 8 bits. We used Larq library to define and train these models. In particular, in our topology some layers/activations are kept in 8 bits while others are in binary. Please note that since this quantization is performed during training (Quantization Aware Training), these networks no longer need to be converted with tensorflow lite. STM32Cube.AI is able to import them directly in .h5 format and to generate the corresponding optimized FW code. Even if many layers are in binary, these models provide comparable accuracy to the full 8-bit ResNet v1 8 but have a significantly lower inference time.

Network information

Network Information Value
Framework TensorFlow Lite
Quantization int8
Provenance https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet
Paper https://arxiv.org/abs/1512.03385

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 Optimized
STM32L0 [] []
STM32L4 [x] []
STM32U5 [x] []
STM32H7 [x] [x]
STM32MP1 [x] [x]*
STM32MP2 [x] []
  • Only for Cifar 100 models

Performances

Training

To train a ResNet v1 model with pretrained weights, from scratch or fine tune it on your own dataset, you need to configure the user_config.yaml file following the tutorial under the src section.

As an example, resnet_v1_8_32_tfs_config.yaml file is used to train this model on Cifar 10 dataset.

For ST ResNet 8 Hybrid v1 or ST ResNet 8 Hybrid v2 please use respectively st_resnet_8_hybrid_v1_32_tfs_config.yaml or st_resnet_8_hybrid_v2_32_tfs_config.yaml instead.

Deployment

To deploy your trained model, you need to configure the user_config.yaml file following the tutorial under the deployment section.

Metrics

Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.

Reference MCU memory footprint based on Cifar 10 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
ResNet v1 8 tfs Int8 32x32x3 STM32H7 62.51 KiB 7.21 KiB 76.9 KiB 56.45 KiB 69.72 KiB 133.35 KiB 9.1.0
ST ResNet 8 Hybrid v1 tfs Hybrid 32x32x3 STM32H7 77.84 KiB 18.38 KiB 85.79 KiB 61.75 KiB 96.22 KiB 147.54 KiB 9.1.0
ST ResNet 8 Hybrid v2 tfs Hybrid 32x32x3 STM32H7 78.99 KiB 18.38 KiB 66.28 KiB 60.99 KiB 97.37 KiB 127.27 KiB 9.1.0

Reference MCU inference time based on Cifar 10 dataset (see Accuracy for details on dataset)

Model Format Resolution Board Execution Engine Frequency Inference time (ms) STM32Cube.AI version
ResNet v1 8 tfs Int8 32x32x3 STM32H747I-DISCO 1 CPU 400 MHz 28.67 ms 9.1.0
ST ResNet 8 Hybrid v1 tfs Hybrid 32x32x3 STM32H747I-DISCO 1 CPU 400 MHz 28.93 ms 9.1.0
ST ResNet 8 Hybrid v2 tfs Hybrid 32x32x3 STM32H747I-DISCO 1 CPU 400 MHz 25.2 ms 9.1.0

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
ResNet v1 8 tfs Int8 32x32x3 per-channel** STM32MP257F-DK2 NPU/GPU 800 MHz 2.02 ms 12.26 87.74 0 v5.1.0 OpenVX
ST ResNet 8 Hybrid v1 tfs Hybrid 32x32x3 per-channel** STM32MP257F-DK2 NPU/GPU 800 MHz TBD ms 0 0 0 v5.1.0 OpenVX
ST ResNet 8 Hybrid v2 tfs Hybrid 32x32x3 per-channel** STM32MP257F-DK2 NPU/GPU 800 MHz TBD ms 0 0 0 v5.1.0 OpenVX
ResNet v1 8 tfs Int8 32x32x3 per-channel STM32MP157F-DK2 2 CPU 800 MHz 6.50 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
ST ResNet 8 Hybrid v1 tfs Hybrid 32x32x3 per-channel STM32MP157F-DK2 2 CPU 800 MHz TBD ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
ST ResNet 8 Hybrid v2 tfs Hybrid 32x32x3 per-channel STM32MP157F-DK2 2 CPU 800 MHz TBD ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
ResNet v1 8 tfs Int8 32x32x3 per-channel STM32MP135F-DK2 1 CPU 1000 MHz 10.77 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
ST ResNet 8 Hybrid v1 tfs Hybrid 32x32x3 per-channel STM32MP135F-DK2 1 CPU 1000 MHz TBD ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
ST ResNet 8 Hybrid v2 tfs Hybrid 32x32x3 per-channel STM32MP135F-DK2 1 CPU 1000 MHz TBD ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0

** To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization

Reference MCU memory footprint based on Cifar 100 dataset (see Accuracy for details on dataset)

Model Format Resolution Series Activation RAM Runtime RAM Weights Flash Code Flash Total RAM Total Flash
ResNet v1 32 tfs Int8 32x32x3 STM32H7 45.41 KiB 24.98 KiB 464.38 KiB 78.65 KiB 70.39 KiB 543.03 KiB

Reference MCU inference time based on Cifar 100 dataset (see Accuracy for details on dataset)

Model Format Resolution Board Execution Engine Frequency Inference time (ms)
ResNet v1 32 tfs Int8 32x32x3 STM32H747I-DISCO 1 CPU 400 MHz 177.7 ms

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
ResNet v1 32 tfs Int8 32x32x3 per-channel STM32MP257F-DK2 NPU/GPU 800 MHz 9.160 ms 14.75 85.25 0 v5.1.0 OpenVX
ResNet v1 32 tfs Int8 32x32x3 per-channel STM32MP157F-DK2 2 CPU 800 MHz 34.78 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
ResNet v1 32 tfs Int8 32x32x3 per-channel STM32MP135F-DK2 1 CPU 1000 MHz 55.32 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0

Accuracy with Cifar10 dataset

Dataset details: link , License CC BY 4.0 , Quotation[1] , Number of classes: 10, Number of images: 60 000

Model Format Resolution Top 1 Accuracy
ResNet v1 8 tfs Float 32x32x3 87.01 %
ResNet v1 8 tfs Int8 32x32x3 85.59 %
ST ResNet 8 Hybrid v1 tfs Hybrid 32x32x3 86 %
ST ResNet 8 Hybrid v2 tfs Hybrid 32x32x3 84.85 %

Accuracy with Cifar100 dataset

Dataset details: link , License CC0 4.0, Quotation[2] , Number of classes:100, Number of images: 600 000

Model Format Resolution Top 1 Accuracy
ResNet v1 32 tfs Float 32x32x3 67.75 %
ResNet v1 32 tfs Int8 32x32x3 66.58 %

Retraining and code generation

Please refer to the yaml explanations: here

Demos

Integration in a simple example

Please refer to the generic guideline here

References

[1] "Tf_flowers : tensorflow datasets," TensorFlow. [Online]. Available: https://www.tensorflow.org/datasets/catalog/tf_flowers.

[2] 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

[3] L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014.