Hugging Face's logo

STMicroelectronics
/
mobilenetv1

Image Classification
Model card Files
xet
Community
mobilenetv1 / README.md
FBAGSTM's picture
FBAGSTM
Release AI-ModelZoo-4.0.0
6ee346b verified
preview code
|
raw
history blame contribute delete
28.9 kB
 ---
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
---
# MobileNet v1
## **Use case** : `Image classification`
# Model description
MobileNet is a well known architecture that can be used in multiple use cases.
Input size and width factor called `alpha` are parameters to be adapted to various use cases complexity. The `alpha` parameter is used to increase or decrease the number of filters in each layer, allowing also to reduce the number of multiply-adds and then the inference time.
The original paper demonstrates the performance of MobileNet models using `alpha` values of 1.0, 0.75, 0.5 and 0.25.
(source: https://keras.io/api/applications/mobilenet/)
The model is quantized in int8 using tensorflow lite converter.
## Network information
| Network Information | Value |
|-------------------------|-----------------|
| Framework | TensorFlow Lite |
| MParams alpha=1.0 | 1.3 M |
| Quantization | int8 |
| Provenance | https://www.tensorflow.org/api_docs/python/tf/keras/applications/mobilenet |
| Paper | https://arxiv.org/abs/1704.04861 |
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 |[x]|[]|
| STM32U5 |[x]|[]|
| STM32H7 |[x]|[x]|
| STM32MP1 |[x]|[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 |
|----------|------------------|--------|-------------|------------------|------------------|---------------------|---------------|-------------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | food101 | Int8 | 224x224x3 | STM32N6 | 392 | 0.0 | 240.88 | 3.0.0 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | food101 | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 864.99 | 3.0.0 |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a100_224_fft/mobilenetv1_a100_224_fft_int8.tflite) | food101 | Int8 | 224x224x3 | STM32N6 | 1568 | 0.0 | 3347.59 | 3.0.0 |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a025_224/mobilenetv1_a025_224_int8.tflite) | imagenet | Int8 | 224x224x3 | STM32N6 | 392 | 0.0 | 469.53 | 3.0.0 |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_int8.tflite) | imagenet | Int8 | 224x224x3 | STM32N6 | 588 | 0.0 | 1318.38 | 3.0.0 |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_qdq_w4_38.8%_w8_61.2%_a8_100%_acc_60.87.onnx) | imagenet | Int8/Int4 | 224x224x3 | STM32N6 | 588 | 0.0 | 1067.95 | 3.0.0 |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a100_224/mobilenetv1_a100_224_int8.tflite) | imagenet | Int8 | 224x224x3 | STM32N6 | 1568 | 0.0 | 4250.49 | 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 |
|--------|----------|--------|-------------|------------------|------------------|---------------------|-----------|-------------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | food101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 2.37 | 421.94 | 3.0.0 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | food101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 5.38 | 185.87 | 3.0.0 |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a100_224_fft/mobilenetv1_a100_224_fft_int8.tflite) | food101 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 16.36 | 61.12 | 3.0.0 |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a025_224/mobilenetv1_a025_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 3.1 | 322.58 | 3.0.0 |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 6.67 | 149.92 | 3.0.0 |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_qdq_w4_38.8%_w8_61.2%_a8_100%_acc_60.87.onnx) | Imagenet | Int8/Int4 | 224x224x3 | STM32N6570-DK | NPU/MCU | 5.95 | 168.07 | 3.0.0 |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a100_224/mobilenetv1_a100_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 18.81 | 53.16 | 3.0.0 |
### Reference **MCU** memory footprint based on Flowers dataset 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 |
|-------------|--------|--------|------------|---------|----------------|---------------|------------|-------------|-------------|-------------|------------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) |tf_flowers| Int8 | 224x224x3 | STM32H7 | 271.04 KiB | 0.7 KiB | 214.69 KiB | 36.07 KiB | 271.74 KiB | 250.76 KiB | 3.0.0 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | tf_flowers| Int8 | 224x224x3 | STM32H7 | 456.67 KiB | 0.7 KiB | 812.61 KiB | 46.79 KiB | 457.37 KiB | 859.4 KiB | 3.0.0 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_fft/mobilenetv1_a025_96_fft_int8.tflite) | tf_flowers | Int8 | 96x96x3 | STM32H7 | 63.04 KiB | 0.7 KiB | 214.69 KiB | 36.03 KiB | 63.74 KiB | 250.72 KiB | 3.0.0 |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_grayscale_tfs/mobilenetv1_a025_96_grayscale_tfs_int8.tflite) | tf_flowers| Int8 | 96x96x1 | STM32H7 | 52.8 KiB | 0.3 KiB | 214.55 KiB | 39.13 KiB | 53.1 KiB | 253.68 KiB | 3.0.0 |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a025_224/mobilenetv1_a025_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32H7 | 267.2 KiB | 0.3 KiB | 467.33 KiB | 37.61 KiB | 267.5 KiB | 504.94 KiB | 3.0.0 |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32H7 | 431.07 KiB | 0.3 KiB | 1314 KiB | 48.32 KiB | 431.37 KiB | 1362.32 KiB | 3.0.0 |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a100_224/mobilenetv1_a100_224_int8.tflite) | Imagenet | Int8 | 224x224x3 | STM32H7 | 899.78 KiB | 0.3 KiB | 4157.09 KiB | 69.82 KiB | 900.08 KiB | 4226.91 KiB | 3.0.0 |
### Reference **MCU** inference time based on Flowers dataset and imagenet dataset (see Accuracy for details on dataset)
| Model | Dataset | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STEdgeAI Core version |
|------------|------|--------|------------|------------------|------------------|-----------|---------------------|------------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | tf_flowers | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 176.26 ms | 3.0.0 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | tf_flowers | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 488.31 ms | 3.0.0 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_fft/mobilenetv1_a025_96_fft_int8.tflite) | tf_flowers| Int8 | 96x96x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 32.64 ms | 3.0.0 |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_grayscale_tfs/mobilenetv1_a025_96_grayscale_tfs_int8.tflite) | tf_flowers| Int8 | 96x96x1 | STM32H747I-DISCO | 1 CPU | 400 MHz | 29.62 ms | 3.0.0 |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a025_224/mobilenetv1_a025_224_int8.tflite) | Imagenet| Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 180.87 ms | 3.0.0 |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_int8.tflite) | Imagenet| Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 504.08 ms | 3.0.0 |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a100_224/mobilenetv1_a100_224_int8.tflite) | Imagenet| Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 1651.05 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 |
|-----------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|------|-------|------|--------------------|-----------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 14.06 | 7.47 | 92.53 | 0 | v6.1.0 | OpenVX |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 32.37 | 3.84 | 96.16 | 0 | v6.1.0 | OpenVX |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_fft/mobilenetv1_a025_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 3.62 | 18.33| 81.67 | 0 | v6.1.0 | OpenVX |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_grayscale_tfs/mobilenetv1_a025_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 3.72 | 14.97| 85.03 | 0 | v6.1.0 | OpenVX |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 31.70 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 89.23 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_fft/mobilenetv1_a025_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 5.99 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_grayscale_tfs/mobilenetv1_a025_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 5.94 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 49.86 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 142.62 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_fft/mobilenetv1_a025_96_fft_int8.tflite) | Int8 | 96x96x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 9.18 | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_grayscale_tfs/mobilenetv1_a025_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 9.24 | 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.
### Accuracy with Flowers dataset
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
| Model | Format | Resolution | Top 1 Accuracy |
|-------|--------|------------|----------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft.keras) | Float | 224x224x3 | 93.05 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | Int8 | 224x224x3 | 92.1 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft.keras) | Float | 224x224x3 | 95.1 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | Int8 | 224x224x3 | 94.41 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_fft/mobilenetv1_a025_96_fft.keras) | Float | 96x96x3 | 87.47 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_fft/mobilenetv1_a025_96_fft_int8.tflite) | Int8 | 96x96x3 | 87.06 % |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_grayscale_tfs/mobilenetv1_a025_96_grayscale_tfs.keras) | Float | 96x96x1 | 74.93 % |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/tf_flowers/mobilenetv1_a025_96_grayscale_tfs/mobilenetv1_a025_96_grayscale_tfs_int8.tflite) | Int8 | 96x96x1 | 74.93 % |
### Accuracy with Plant-village dataset
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
| Model | Format | Resolution | Top 1 Accuracy |
|-------|--------|------------|----------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant_leaf_diseases/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft.keras) | Float | 224x224x3 | 99.95 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant_leaf_diseases/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | Int8 | 224x224x3 | 99.82 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant_leaf_diseases/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft.keras) | Float | 224x224x3 | 99.94 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant_leaf_diseases/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | Int8 | 224x224x3 | 99.85 % |
### Accuracy with Food-101 dataset
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
| Model | Format | Resolution | Top 1 Accuracy |
|-------|--------|------------|----------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft.keras) | Float | 224x224x3 | 75.75 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a025_224_fft/mobilenetv1_a025_224_fft_int8.tflite) | Int8 | 224x224x3 | 73.24 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft.keras) | Float | 224x224x3 | 82.06 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a050_224_fft/mobilenetv1_a050_224_fft_int8.tflite) | Int8 | 224x224x3 | 80.64 % |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a100_224_fft/mobilenetv1_a100_224_fft.keras) | Float | 224x224x3 | 84.57 % |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food101/mobilenetv1_a100_224_fft/mobilenetv1_a100_224_fft_int8.tflite) | Int8 | 224x224x3 | 83.07 % |
### 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 |
|---------|--------|------------|----------------|
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a025_224/mobilenetv1_a025_224.keras) | Float | 224x224x3 | 50.5 % |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a025_224/mobilenetv1_a025_224_int8.tflite) | Int8 | 224x224x3 | 47.94 % |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224.keras) | Float | 224x224x3 | 64.02 % |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_int8.tflite) | Int8 | 224x224x3 | 62.25 % |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a050_224/mobilenetv1_a050_224_qdq_w4_38.8%_w8_61.2%_a8_100%_acc_60.87.onnx) | Int8/Int4 | 224x224x3 | 60.87 % |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a100_224/mobilenetv1_a100_224.keras) | Float | 224x224x3 | 70.92 % |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/imagenet/mobilenetv1_a100_224/mobilenetv1_a100_224_int8.tflite) | Int8 | 224x224x3 | 69.64 % |
## 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.