| | --- |
| | license: other |
| | license_name: sla0044 |
| | license_link: >- |
| | https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/LICENSE.md |
| | --- |
| | # 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. |
| |
|
| |
|
| | ## 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] | [] | |
| | | STM32N6 | [x] | [] | |
| | |
| | * Only for Cifar 100 models |
| | |
| | # 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 **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 | STEdgeAI Core version | |
| | |----------|--------|-------------|---------|----------------|-------------|---------------|------------|-----------|-------------|------------------------| |
| | | [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar10/resnet8_32_tfs/resnet8_32_tfs_int8.tflite) | Int8 | 32x32x3 | STM32H7 | 62.51 KiB | 1.26 KiB | 76.9 KiB | 36.08 KiB | 63.77 KiB | 112.98 KiB | 3.0.0 | |
| | |
| | |
| | ### 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) | STEdgeAI Core version | |
| | |----------------------------------|--------|-------------|------------------|------------------|--------------|---------------------|------------------------| |
| | | [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar10/resnet8_32_tfs/resnet8_32_tfs_int8.tflite) | Int8 | 32x32x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 28.71 ms | 3.0.0 | |
| | |
| | |
| | ### 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar10/resnet8_32_tfs/resnet8_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 2.06 | 21.76 | 78.24 | 0 | v6.1.0 | OpenVX | |
| | | [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar10/resnet8_32_tfs/resnet8_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 6.71 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
| | | [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar10/resnet8_32_tfs/resnet8_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 10.34 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
| | |
| | |
| | ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization** |
| | |
| | ** **Note:** On STM32MP2 devices, per-channel quantized models are internally converted to per-tensor quantization by the compiler using an entropy-based method. This may introduce a slight loss in accuracy compared to the original per-channel models. |
| | |
| | ### 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 | STEdgeAI Core version | |
| | |-----------|--------|-------------|---------|----------------|-------------|---------------|------------|-------------|-------------|------------------------| |
| | | [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar100/resnet32_32_tfs/resnet32_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 | 3.0.0 | |
| | |
| | |
| | ### 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) | STEdgeAI Core version | |
| | |---------|--------|------------|------------------|------------------|--------------|---------------------|------------------------| |
| | | [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar100/resnet32_32_tfs/resnet32_32_tfs_int8.tflite) | Int8 | 32x32x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 177.7 ms |3.0.0 | |
| | |
| | |
| | ### 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 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar100/resnet32_32_tfs/resnet32_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 9.160 ms | 14.75 | 85.25 | 0 | v6.1.0 | OpenVX | |
| | |[ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar100/resnet32_32_tfs/resnet32_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 34.78 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.11.0 | |
| | |[ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar100/resnet32_32_tfs/resnet32_32_tfs_int8.tflite) | Int8 | 32x32x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 55.32 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.11.0 | |
| | |
| | |
| | ### Accuracy with Cifar10 dataset |
| | |
| | Dataset details: [link](https://www.cs.toronto.edu/~kriz/cifar.html) , |
| | License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) , Quotation[[1]](#1) , Number of classes: 10, Number of |
| | images: 60 000 |
| | |
| | | Model | Format | Resolution | Top 1 Accuracy | |
| | |------------------------------------------------------------------------------------------------------------------|----------|-------------|----------------| |
| | | [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar10/resnet8_32_tfs/resnet8_32_tfs.keras) | Float | 32x32x3 | 87.01 % | |
| | | [ResNet v1 8 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar10/resnet8_32_tfs/resnet8_32_tfs_int8.tflite) | Int8 | 32x32x3 | 85.59 % | |
| | |
| | |
| | ### Accuracy with Cifar100 dataset |
| | |
| | Dataset details: [link](https://www.cs.toronto.edu/~kriz/cifar.html) , |
| | License [CC0 4.0](https://creativecommons.org/licenses/by/4.0/), Quotation[[2]](#2) , Number of classes:100, |
| | Number of images: 600 000 |
| | |
| | | Model | Format | Resolution | Top 1 Accuracy | |
| | |----------------------------------------------------------------------------------------------------------------------|---------|------------|----------------| |
| | | [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar100/resnet32_32_tfs/resnet32_32_tfs.keras) | Float | 32x32x3 | 67.75 % | |
| | | [ResNet v1 32 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/resnet/ST_pretrainedmodel_public_dataset/cifar100/resnet32_32_tfs/resnet32_32_tfs_int8.tflite) | Int8 | 32x32x3 | 66.58 % | |
| | |
| | ## 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> |
| | "Tf_flowers : tensorflow datasets," TensorFlow. [Online]. Available: https://www.tensorflow.org/datasets/catalog/tf_flowers. |
| | |
| | <a id="2">[2]</a> |
| | 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 |
| | |
| | <a id="3">[3]</a> |
| | L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014. |
