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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/raw/refs/heads/main/image_classification/LICENSE.md |
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pipeline_tag: image-classification |
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--- |
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# Squeezenet v1.1 |
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## **Use case** : `Image classification` |
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# Model description |
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SqueezeNet is a convolutional neural network that uses design strategies to reduce the number of parameters, particularly with the use of fire modules that "squeeze" parameters using 1x1 convolutions. |
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SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrifying accuracy. |
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The model is quantized in int8 using tensorflow lite converter. |
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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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| MParams | 725,061 | |
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| Quantization | int8 | |
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| Provenance | https://github.com/forresti/SqueezeNet | |
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| Paper | https://arxiv.org/pdf/1602.07360.pdf | |
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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]|[]| |
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| STM32MP2 |[x]|[]| |
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| STM32N6 |[x]|[]| |
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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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### Reference **NPU** memory footprint on food-101 dataset (see Accuracy for details on dataset) |
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|Model | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version | |
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|----------|--------|-------------|------------------|------------------|---------------------|---------------|----------------------|-------------------------| |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6 | 270.28 | 0.0 | 753.38 | 10.2.0 | 2.2.0 | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6 | 858.23 | 0.0 | 753.38 | 10.2.0 | 2.2.0 | |
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### Reference **NPU** inference time on food-101 dataset (see Accuracy for details on dataset) |
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| Model | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version | |
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|--------|--------|-------------|------------------|------------------|---------------------|-----------|----------------------|-------------------------| |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 4.06 | 246.3 | 10.2.0 | 2.2.0 | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 8.7 | 111.94 | 10.2.0 | 2.2.0 | |
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### Reference **MCU** memory footprint based on Flowers 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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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32H7 | 271.84 KiB | 16.47 KiB | 716.71 KiB | 71.02 KiB | 288.31 KiB | 787.73 KiB | 10.2.0 | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 816.86 KiB | 16.52 KiB | 716.71 KiB | 71.1 KiB | 833.38 KiB | 787.81 KiB | 10.2.0 | |
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### Reference **MCU** inference time based on Flowers 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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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 216.06 ms | 10.2.0 | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 693.41 ms | 10.2.0 | |
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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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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 9.76 ms | 8.46 | 91.54 | 0 | v6.1.0 | OpenVX | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 31.11 ms | 8.31 | 91.69 | 0 | v6.1.0 | OpenVX | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 44.91 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 144.10 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 73.02 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 232.37 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 | |
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** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization** |
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### Accuracy with Flowers dataset |
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Dataset details: [link](http://download.tensorflow.org/example_images/flower_photos.tgz) , License [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/) , Quotation[[1]](#1) , Number of classes: 5, Number of images: 3 670 |
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| Model | Format | Resolution | Top 1 Accuracy | |
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|---------|--------|------------|--------------| |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs.h5) | Float | 224x224x3 | 85.29 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | 83.24 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs.h5) | Float | 128x128x3 | 80.93 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/flowers/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | 80.93 % | |
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### Accuracy with Food-101 dataset |
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Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/), Quotation[[3]](# 3) , Number of classes: 101 , Number of images: 101 000 |
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| Model | Format | Resolution | Top 1 Accuracy | |
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|---------|--------|------------|----------------| |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs.h5) | Float | 224x224x3 | 67.17 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | 66.71 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs.h5) | Float | 128x128x3 | 58.54 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/food-101/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | 58.51 % | |
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### Accuracy with Plant-village dataset |
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Dataset details: [link](https://data.mendeley.com/datasets/tywbtsjrjv/1) , License [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/), Quotation[[2]](#2) , Number of classes: 39, Number of images: 61 486 |
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| Model | Format | Resolution | Top 1 Accuracy | |
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|---------|--------|------------|---------------| |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/plant-village/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs.h5) | Float | 224x224x3 | 99.88 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/plant-village/squeezenetv1.1_224_tfs/squeezenetv1.1_224_tfs_int8.tflite) | Int8 | 224x224x3 | 99.74 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/plant-village/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs.h5) | Float | 128x128x3 | 99.77 % | |
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| [SqueezeNet v1.1 tfs ](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/squeezenetv1.1/ST_pretrainedmodel_public_dataset/plant-village/squeezenetv1.1_128_tfs/squeezenetv1.1_128_tfs_int8.tflite) | Int8 | 128x128x3 | 99.69 % | |
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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. |
