---
license: other
license_name: sla0044
license_link: >-
  https://github.com/STMicroelectronics/stm32ai-modelzoo/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 food-101 and ImageNet dataset (see Accuracy for details on dataset)
|Model      | Dataset       | Format   | Resolution | Series    | Internal RAM | External RAM | Weights Flash | STM32Cube.AI version | STEdgeAI Core version |
|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6   | 588 | 0.0 | 321.66 |       10.0.0        |     2.0.0   |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6   | 588 | 0.0 | 1025.63 |       10.0.0        |     2.0.0   |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6   | 1568 | 0.0 | 3649.97 |       10.0.0        |     2.0.0   |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | ImageNet      | Int8     | 224x224x3  | STM32N6   | 588 | 0.0 | 549.88 |       10.0.0        |     2.0.0   |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | ImageNet      | Int8     | 224x224x3  | STM32N6   | 588 | 0.0 | 1478.58 |       10.0.0        |     2.0.0   |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | ImageNet      | Int8     | 224x224x3  | STM32N6   | 1568 | 0.0 | 4552.42 |       10.0.0        |     2.0.0   |

### Reference **NPU**  inference time on food-101 and ImageNet dataset (see Accuracy for details on dataset)


| Model  | Dataset          | Format | Resolution  | Board            | Execution Engine | Inference time (ms) | Inf / sec   | STM32Cube.AI version  |  STEdgeAI Core version |
|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU      | 2.83 | 353.36 |       10.0.0        |     2.0.0   |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU      | 6.06 | 165.02 |       10.0.0        |     2.0.0   |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU      | 16.94 | 59.03|       10.0.0        |     2.0.0   |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU      | 3.57 | 280.11 |       10.0.0        |     2.0.0   |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU      | 7.38 | 135.50 |       10.0.0        |     2.0.0   |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | food-101      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU      | 19.41 | 51.53 |       10.0.0        |     2.0.0   |


### Reference **MCU** memory footprint based on Flowers dataset and ImageNet 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  |
|--------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8   | 224x224x3    | STM32H7 | 272.96 KiB     | 16.38 KiB      | 214.69 KiB    | 68.05 KiB  | 289.34 KiB   | 282.74 KiB  | 10.0.0  |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite)    | Int8   | 224x224x3    | STM32H7 | 449.58 KiB     | 16.38 KiB      | 812.61 KiB    | 81.46 KiB  | 465.96 KiB   | 894.02 KiB  | 10.0.0 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite)   | Int8   | 96x96x3    | STM32H7 | 66.96 KiB      | 16.33 KiB     | 214.69 KiB       | 68 KiB     | 83.29 KiB    | 282.69 KiB  | 10.0.0                 |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite)   | Int8   | 96x96x1    | STM32H7 | 52.8 KiB      | 16.33 KiB     | 214.55 KiB    | 70.27 KiB    | 69.13 KiB | 284.82 KiB  | 10.0.0                 |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | Int8   | 224x224x3    | STM32H7 | 272.96 KiB     | 16.43 KiB      | 467.33 KiB    | 70.02 KiB  | 283.63 KiB   | 537.35 KiB  | 10.0.0  |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite)    | Int8   | 224x224x3    | STM32H7 | 431.07 KiB     | 16.43 KiB      | 1314 KiB    | 83.38 KiB  | 447.5 KiB   | 1397.38 KiB  | 10.0.0 |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite)    | Int8   | 224x224x3    | STM32H7 | 1331.13 KiB     | 16.48 KiB      | 4157.09 KiB    | 110.11 KiB  | 1347.61 KiB   | 4267.2 KiB  | 10.0.0 |


### Reference **MCU** inference time based on Flowers dataset and ImageNet dataset (see Accuracy for details on dataset)


| Model             | Format | Resolution | Board            | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version  |
|-------------------|--------|------------|------------------|------------------|-----------|------------------|-----------------------|
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8   | 224x224x3   | STM32H747I-DISCO | 1 CPU            | 400 MHz   | 163.78 ms        | 10.0.0                 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8   | 224x224x3    | STM32H747I-DISCO | 1 CPU            | 400 MHz   | 485.79 ms        | 10.0.0                 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite) | Int8   | 96x96x3    | STM32H747I-DISCO  | 1 CPU            | 400 MHz   | 29.94 ms         | 10.0.0                 |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8   | 96x96x1    | STM32H747I-DISCO  | 1 CPU            | 400 MHz   | 28.34 ms            | 10.0.0                 |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite) | Int8   | 224x224x3   | STM32H747I-DISCO | 1 CPU            | 400 MHz   | 166.75 ms        | 10.0.0                 |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite) | Int8   | 224x224x3   | STM32H747I-DISCO | 1 CPU            | 400 MHz   | 504.37 ms        | 10.0.0                 |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite) | Int8   | 224x224x3   | STM32H747I-DISCO | 1 CPU            | 400 MHz   | 1641.84 ms        | 10.0.0                 |


### 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/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite)  | Int8   | 224x224x3  |  per-channel**  | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 14.29  ms            | 6.04    | 93.96 | 0 | v5.1.0             | OpenVX                |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8   | 224x224x3  |  per-channel**  | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 32.74  ms            | 3.41    | 96.59 | 0 | v5.1.0             | OpenVX                |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite)  | Int8   | 96x96x3  |  per-channel**  | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 3.740  ms            | 14.20    | 85.80 | 0 | v5.1.0             | OpenVX                |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8   | 96x96x1  |  per-channel**  | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 3.68  ms            | 11.47    | 88.53 | 0 | v5.1.0             | OpenVX                |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite)  | Int8   | 224x224x3  |  per-channel  | STM32MP157F-DK2   | 2 CPU            | 800  MHz  | 33.97 ms            | NA  | NA    | 100    | v5.1.0             | TensorFlowLite 2.11.0 |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8   | 224x224x3  |  per-channel  | STM32MP157F-DK2   | 2 CPU            | 800  MHz  | 91.42 ms            | NA  | NA    | 100    | v5.1.0             | TensorFlowLite 2.11.0 |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite)  | Int8   | 96x96x3  |  per-channel  | STM32MP157F-DK2   | 2 CPU            | 800  MHz  | 6.40  ms            | NA  | NA    | 100    | v5.1.0             | TensorFlowLite 2.11.0 |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8   | 96x96x1  |  per-channel  | STM32MP157F-DK2   | 2 CPU            | 800  MHz  | 5.83 ms            | NA  | NA    | 100    | v5.1.0             | TensorFlowLite 2.11.0 |
|[MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite)  | Int8   | 224x224x3  |  per-channel  | STM32MP135F-DK2   | 1 CPU            | 1000 MHz  | 52.51 ms            | NA  | NA    | 100    | v5.1.0             | TensorFlowLite 2.11.0 |
|[MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8   | 224x224x3  |  per-channel  | STM32MP135F-DK2   | 1 CPU            | 1000 MHz  | 145.4 ms            | NA  | NA    | 100    | v5.1.0             | TensorFlowLite 2.11.0 |
|[MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite)  | Int8   | 96x96x3  |  per-channel  | STM32MP135F-DK2   | 1 CPU            | 1000 MHz  | 9.75 ms            | NA  | NA    | 100    | v5.1.0             | TensorFlowLite 2.11.0 |
|[MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs_int8.tflite) | Int8   | 96x96x1  |  per-channel  | STM32MP135F-DK2   | 1 CPU            | 1000 MHz  | 9.01 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**

### 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 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_tfs/mobilenet_v1_0.25_224_tfs.h5) | Float | 224x224x3    | 88.83 % |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_tfs/mobilenet_v1_0.25_224_tfs_int8.tflite) | Int8 | 224x224x3    | 89.37 % |
| [MobileNet v1 0.25 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_tl/mobilenet_v1_0.25_224_tl.h5) | Float | 224x224x3    | 85.83 % |
| [MobileNet v1 0.25 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_tl/mobilenet_v1_0.25_224_tl_int8.tflite) | Int8 | 224x224x3    | 83.24 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft.h5) | Float | 224x224x3    | 93.05 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3    | 92.1 % |
| [MobileNet v1 0.5 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_tfs/mobilenet_v1_0.5_224_tfs.h5) | Float | 224x224x3    | 92.1 % |
| [MobileNet v1 0.5 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_tfs/mobilenet_v1_0.5_224_tfs_int8.tflite) | Int8 | 224x224x3    | 91.55 % |
| [MobileNet v1 0.5 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_tl/mobilenet_v1_0.5_224_tl.h5) | Float | 224x224x3    | 88.56 % |
| [MobileNet v1 0.5 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_tl/mobilenet_v1_0.5_224_tl_int8.tflite) | Int8 | 224x224x3    | 87.74 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft.h5) | Float | 224x224x3    | 95.1 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3    | 94.41 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft.h5)      | Float  | 96x96x3      | 87.47 %   |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_fft/mobilenet_v1_0.25_96_fft_int8.tflite)   | Int8   | 96x96x3    | 87.06  % |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_96_grayscale_tfs.h5) | Float | 96x96x1 | 74.93 % |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v1_0.25_96_grayscale_tfs/mobilenet_v1_0.25_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 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.25_224_tfs/mobilenet_v1_0.25_224_tfs.h5) | Float | 224x224x3    | 99.92 % |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.25_224_tfs/mobilenet_v1_0.25_224_tfs_int8.tflite) | Int8 | 224x224x3    | 99.92 % |
| [MobileNet v1 0.25 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.25_224_tl/mobilenet_v1_0.25_224_tl.h5) | Float | 224x224x3    | 85.38 % |
| [MobileNet v1 0.25 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.25_224_tl/mobilenet_v1_0.25_224_tl_int8.tflite) | Int8 | 224x224x3    | 83.7 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft.h5) | Float | 224x224x3    | 99.95 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3    | 99.82 % |
| [MobileNet v1 0.5 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.5_224_tfs/mobilenet_v1_0.5_224_tfs.h5) | Float | 224x224x3    | 99.9 % |
| [MobileNet v1 0.5 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.5_224_tfs/mobilenet_v1_0.5_224_tfs_int8.tflite) | Int8 | 224x224x3    | 99.83 % |
| [MobileNet v1 0.5 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.5_224_tl/mobilenet_v1_0.5_224_tl.h5) | Float | 224x224x3    | 93.05 % |
| [MobileNet v1 0.5 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.5_224_tl/mobilenet_v1_0.5_224_tl_int8.tflite) | Int8 | 224x224x3    | 92.7 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft.h5) | Float | 224x224x3    | 99.94 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/plant-village/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_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 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_tfs/mobilenet_v1_0.25_224_tfs.h5) | Float | 224x224x3    | 72.16 % |
| [MobileNet v1 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_tfs/mobilenet_v1_0.25_224_tfs_int8.tflite) | Int8 | 224x224x3    | 71.13 % |
| [MobileNet v1 0.25 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_tl/mobilenet_v1_0.25_224_tl.h5) | Float | 224x224x3    | 43.21 % |
| [MobileNet v1 0.25 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_tl/mobilenet_v1_0.25_224_tl_int8.tflite) | Int8 | 224x224x3    | 39.89 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft.h5) | Float | 224x224x3    | 72.36 % |
| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.25_224_fft/mobilenet_v1_0.25_224_fft_int8.tflite) | Int8 | 224x224x3    | 69.52 % |
| [MobileNet v1 0.5 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_tfs/mobilenet_v1_0.5_224_tfs.h5) | Float | 224x224x3    | 76.97 % |
| [MobileNet v1 0.5 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_tfs/mobilenet_v1_0.5_224_tfs_int8.tflite) | Int8 | 224x224x3    | 76.37 % |
| [MobileNet v1 0.5 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_tl/mobilenet_v1_0.5_224_tl.h5) | Float | 224x224x3    | 48.78 % |
| [MobileNet v1 0.5 tl](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_tl/mobilenet_v1_0.5_224_tl_int8.tflite) | Int8 | 224x224x3    | 45.89 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft.h5) | Float | 224x224x3    | 76.72 % |
| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_0.5_224_fft/mobilenet_v1_0.5_224_fft_int8.tflite) | Int8 | 224x224x3    | 74.82 % |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft.h5) | Float | 224x224x3    | 80.38 % |
| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/ST_pretrainedmodel_public_dataset/food-101/mobilenet_v1_1.0_224_fft/mobilenet_v1_1.0_224_fft_int8.tflite) | Int8 | 224x224x3    | 79.43 % |


### 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/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224.h5)                                | Float  | 224x224x3  | 48.96 %        |
| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224_int8.tflite)                       | Int8   | 224x224x3  | 46.34 %        |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224.h5)                                | Float  | 224x224x3  | 62.11 %       |
| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_0.5_224/mobilenet_v1_0.5_224_int8.tflite)                       | Int8   | 224x224x3  | 59.92 %       |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224.h5)                                | Float  | 224x224x3  | 69.52 %       |
| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/image_classification/mobilenetv1/Public_pretrainedmodel_public_dataset/ImageNet/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_int8.tflite)                       | Int8   | 224x224x3  | 68.64 %       |



## 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.