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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/tree/main/image_classification/LICENSE.md |
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pipeline_tag: image-classification |
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--- |
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# ResNet v1 |
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## **Use case** : `Image classification` |
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# Model description |
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ResNet models perform image classification - they take images as input and classify the major object in the image into a |
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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. |
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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. |
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ResNet v1 uses post-activation for the residual blocks. The models below have 8 and 32 layers with ResNet v1 architecture. |
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(source: https://keras.io/api/applications/resnet/) |
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The model is quantized in int8 using tensorflow lite converter. |
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In addition, we introduce a new model family inspired from ResNet v1 which takes benefit from hybrid quantization. |
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Later on, they are named as ST ResNet 8 Hybrid v1 and ST ResNet 8 Hybrid v2. |
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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. |
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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. |
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Please note that since this quantization is performed during training (Quantization Aware Training), these networks no longer need to be converted with tensorflow lite. |
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STM32Cube.AI is able to import them directly in .h5 format and to generate the corresponding optimized FW code. |
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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. |
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## Network information |
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| Network Information | Value | |
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|-------------------------|-------------------------------------------------------------------------| |
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| Framework | TensorFlow Lite | |
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| Quantization | int8 | |
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| Provenance | https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet | |
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| Paper | https://arxiv.org/abs/1512.03385 | |
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The models are quantized using tensorflow lite converter. |
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## Network inputs / outputs |
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For an image resolution of NxM and P classes |
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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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| 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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## Recommended Platforms |
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| Platform | Supported | Optimized | |
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|----------|-----------|-----------| |
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| STM32L0 | [] | [] | |
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| STM32L4 | [x] | [] | |
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| STM32U5 | [x] | [] | |
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| STM32H7 | [x] | [x] | |
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| STM32MP1 | [x] | [x]* | |
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| STM32MP2 | [x] | [] | |
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| STM32N6 | [x] | [] | |
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* Only for Cifar 100 models |
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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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- `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. |
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- `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. |
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### Reference **MCU** memory footprint based on Cifar 10 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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| [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/resnet_v1_8_32_tfs/resnet_v1_8_32_tfs_int8.tflite) | Int8 | 32x32x3 | STM32H7 | 62.51 KiB | 7.21 KiB | 76.9 KiB | 56.45 KiB | 69.72 KiB | 133.35 KiB | 10.0.0 | |
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| [ST ResNet 8 Hybrid v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v1_32_tfs/st_resnet_8_hybrid_v1_32_tfs.h5) | Hybrid | 32x32x3 | STM32H7 | 77.84 KiB | 18.38 KiB | 85.79 KiB | 61.75 KiB | 96.22 KiB | 147.54 KiB | 10.0.0 | |
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| [ST ResNet 8 Hybrid v2 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v2_32_tfs/st_resnet_8_hybrid_v2_32_tfs.h5) | Hybrid | 32x32x3 | STM32H7 | 78.99 KiB | 18.38 KiB | 66.28 KiB | 60.99 KiB | 97.37 KiB | 127.27 KiB | 10.0.0 | |
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### Reference **MCU** inference time based on Cifar 10 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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| [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/resnet_v1_8_32_tfs/resnet_v1_8_32_tfs_int8.tflite) | Int8 | 32x32x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 28.67 ms | 10.0.0 | |
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| [ST ResNet 8 Hybrid v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v1_32_tfs/st_resnet_8_hybrid_v1_32_tfs.h5) | Hybrid | 32x32x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 28.93 ms | 10.0.0 | |
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| [ST ResNet 8 Hybrid v2 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v2_32_tfs/st_resnet_8_hybrid_v2_32_tfs.h5) | Hybrid | 32x32x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 25.2 ms | 10.0.0 | |
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### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset) |
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| Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework | |
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|---------------------------------------------------------------------------------------------------------------------------------------|----------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------| |
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| [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/resnet_v1_8_32_tfs/resnet_v1_8_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 2.02 ms | 12.26 | 87.74 | 0 | v5.1.0 | OpenVX | |
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| [ST ResNet 8 Hybrid v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v1_32_tfs/st_resnet_8_hybrid_v1_32_tfs.h5) | Hybrid | 32x32x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | TBD ms | 0 | 0 | 0 | v5.1.0 | OpenVX | |
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| [ST ResNet 8 Hybrid v2 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v2_32_tfs/st_resnet_8_hybrid_v2_32_tfs.h5) | Hybrid | 32x32x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | TBD ms | 0 | 0 | 0 | v5.1.0 | OpenVX | |
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| [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/resnet_v1_8_32_tfs/resnet_v1_8_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 6.50 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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| [ST ResNet 8 Hybrid v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v1_32_tfs/st_resnet_8_hybrid_v1_32_tfs.h5) | Hybrid | 32x32x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | TBD ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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| [ST ResNet 8 Hybrid v2 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v2_32_tfs/st_resnet_8_hybrid_v2_32_tfs.h5) | Hybrid | 32x32x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | TBD ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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| [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/resnet_v1_8_32_tfs/resnet_v1_8_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 10.77 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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| [ST ResNet 8 Hybrid v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v1_32_tfs/st_resnet_8_hybrid_v1_32_tfs.h5) | Hybrid | 32x32x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | TBD ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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| [ST ResNet 8 Hybrid v2 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v2_32_tfs/st_resnet_8_hybrid_v2_32_tfs.h5) | Hybrid | 32x32x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | TBD ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization** |
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### Reference **MCU** memory footprint based on Cifar 100 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 | |
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|----------------------------------------------------------------------------------------------------------------------|--------|-------------|---------|----------------|-------------|---------------|------------|-------------|-------------| |
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| [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar100/resnet_v1_32_32_tfs/resnet_v1_32_32_tfs_int8.tflite) | Int8 | 32x32x3 | STM32H7 | 45.41 KiB | 24.98 KiB | 464.38 KiB | 78.65 KiB | 70.39 KiB | 543.03 KiB | |
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### Reference **MCU** inference time based on Cifar 100 dataset (see Accuracy for details on dataset) |
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| Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | |
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|----------------------------------------------------------------------------------------------------------------------|--------|------------|------------------|------------------|--------------|---------------------| |
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| [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar100/resnet_v1_32_32_tfs/resnet_v1_32_32_tfs_int8.tflite) | Int8 | 32x32x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 177.7 ms | |
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### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset) |
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| Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework | |
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|---------------------------------------------------------------------------------------------------------------------|----------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------| |
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|[ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar100/resnet_v1_32_32_tfs/resnet_v1_32_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 9.160 ms | 14.75 | 85.25 | 0 | v5.1.0 | OpenVX | |
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|[ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar100/resnet_v1_32_32_tfs/resnet_v1_32_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 34.78 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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|[ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar100/resnet_v1_32_32_tfs/resnet_v1_32_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 55.32 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 | |
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### Accuracy with Cifar10 dataset |
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Dataset details: [link](https://www.cs.toronto.edu/~kriz/cifar.html) , |
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License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) , Quotation[[1]](#1) , Number of classes: 10, Number of |
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images: 60 000 |
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| Model | Format | Resolution | Top 1 Accuracy | |
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| [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/resnet_v1_8_32_tfs/resnet_v1_8_32_tfs.h5) | Float | 32x32x3 | 87.01 % | |
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| [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/resnet_v1_8_32_tfs/resnet_v1_8_32_tfs_int8.tflite) | Int8 | 32x32x3 | 85.59 % | |
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| [ST ResNet 8 Hybrid v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v1_32_tfs/st_resnet_8_hybrid_v1_32_tfs.h5) | Hybrid | 32x32x3 | 86 % | |
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| [ST ResNet 8 Hybrid v2 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar10/st_resnet_8_hybrid_v2_32_tfs/st_resnet_8_hybrid_v2_32_tfs.h5) | Hybrid | 32x32x3 | 84.85 % | |
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### Accuracy with Cifar100 dataset |
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Dataset details: [link](https://www.cs.toronto.edu/~kriz/cifar.html) , |
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License [CC0 4.0](https://creativecommons.org/licenses/by/4.0/), Quotation[[2]](#2) , Number of classes:100, |
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Number of images: 600 000 |
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| Model | Format | Resolution | Top 1 Accuracy | |
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|----------------------------------------------------------------------------------------------------------------------|---------|------------|----------------| |
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| [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar100/resnet_v1_32_32_tfs/resnet_v1_32_32_tfs.h5) | Float | 32x32x3 | 67.75 % | |
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| [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnetv1/ST_pretrainedmodel_public_dataset/cifar100/resnet_v1_32_32_tfs/resnet_v1_32_32_tfs_int8.tflite) | Int8 | 32x32x3 | 66.58 % | |
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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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<a id="1">[1]</a> |
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"Tf_flowers : tensorflow datasets," TensorFlow. [Online]. Available: https://www.tensorflow.org/datasets/catalog/tf_flowers. |
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<a id="2">[2]</a> |
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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 |
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<a id="3">[3]</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. |
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