Release AI-ModelZoo-4.0.0
Browse files
README.md
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- `tl` stands for "transfer learning", meaning that the model backbone weights were initialized from a pre-trained model, then only the last layer was unfrozen during the training.
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- `fft` stands for "full fine-tuning", meaning that the full model weights were initialized from a transfer learning pre-trained model, and all the layers were unfrozen during the training.
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### Reference **NPU** memory footprint on
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|Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash |
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| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.5 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 1.0 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1
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| [MobileNet v1 0.
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| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.
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| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.
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| [MobileNet v1
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| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.
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| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.
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| [MobileNet v1
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### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset)
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| [MobileNet v1 0.
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| [MobileNet v1 0.
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| [MobileNet v1 0.25
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| [MobileNet v1 0.25
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| [MobileNet v1 0.
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| [MobileNet v1 0.
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| [MobileNet v1 0.25
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|[MobileNet v1 0.25
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|[MobileNet v1 0.
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|[MobileNet v1 0.
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|[MobileNet v1 0.25
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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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| Model | Format | Resolution | Top 1 Accuracy |
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|-------|--------|------------|----------------|
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| [MobileNet v1 0.25
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| [MobileNet v1 0.25
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| [MobileNet v1 0.
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| [MobileNet v1 0.
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| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.25 fft](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.
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| [MobileNet v1 0.
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| Model | Format | Resolution | Top 1 Accuracy |
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|-------|--------|------------|----------------|
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| [MobileNet v1 0.25
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| [MobileNet v1 0.25
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| [MobileNet v1 0.
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| [MobileNet v1 0.
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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### Accuracy with Food-101 dataset
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| Model | Format | Resolution | Top 1 Accuracy |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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| [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 % |
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### Accuracy with ImageNet dataset
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Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4).
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Number of classes: 1000.
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|model | Format | Resolution | Top 1 Accuracy |
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| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.25](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 0.5](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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| [MobileNet v1 1.0](https://github.com/STMicroelectronics/stm32ai-modelzoo/
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- `tl` stands for "transfer learning", meaning that the model backbone weights were initialized from a pre-trained model, then only the last layer was unfrozen during the training.
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- `fft` stands for "full fine-tuning", meaning that the full model weights were initialized from a transfer learning pre-trained model, and all the layers were unfrozen during the training.
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### Reference **NPU** memory footprint on food101 and imagenet dataset (see Accuracy for details on dataset)
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|Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STEdgeAI Core version |
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|----------|------------------|--------|-------------|------------------|------------------|---------------------|---------------|-------------------------|
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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### Reference **NPU** inference time on food101 and imagenet dataset (see Accuracy for details on dataset)
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| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version |
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|--------|----------|--------|-------------|------------------|------------------|---------------------|-----------|-------------------------|
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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### Reference **MCU** memory footprint based on Flowers dataset and imagenet dataset (see Accuracy for details on dataset)
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| Model | Dataset| Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STEdgeAI Core version |
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|-------------|--------|--------|------------|---------|----------------|---------------|------------|-------------|-------------|-------------|------------------------|
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| 103 |
+
| [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 |
|
| 104 |
+
| [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 |
|
| 105 |
+
| [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 |
|
| 106 |
+
| [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 |
|
| 107 |
+
| [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 |
|
| 108 |
+
| [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 |
|
| 109 |
+
| [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 |
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
### Reference **MCU** inference time based on Flowers dataset and imagenet dataset (see Accuracy for details on dataset)
|
| 113 |
+
|
| 114 |
+
|
| 115 |
+
| Model | Dataset | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STEdgeAI Core version |
|
| 116 |
+
|------------|------|--------|------------|------------------|------------------|-----------|---------------------|------------------------|
|
| 117 |
+
| [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 |
|
| 118 |
+
| [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 |
|
| 119 |
+
| [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 |
|
| 120 |
+
| [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 |
|
| 121 |
+
| [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 |
|
| 122 |
+
| [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 |
|
| 123 |
+
| [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 |
|
| 124 |
|
| 125 |
|
| 126 |
### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset)
|
| 127 |
+
|
| 128 |
+
| Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
|
| 129 |
+
|-----------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|------|-------|------|--------------------|-----------------------|
|
| 130 |
+
| [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 |
|
| 131 |
+
| [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 |
|
| 132 |
+
| [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 |
|
| 133 |
+
| [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 |
|
| 134 |
+
| [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 |
|
| 135 |
+
| [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 |
|
| 136 |
+
| [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 |
|
| 137 |
+
| [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 |
|
| 138 |
+
| [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 |
|
| 139 |
+
| [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 |
|
| 140 |
+
| [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 |
|
| 141 |
+
| [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 |
|
| 142 |
|
| 143 |
** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
|
| 144 |
|
| 145 |
+
** **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.
|
| 146 |
+
|
| 147 |
### Accuracy with Flowers dataset
|
| 148 |
|
| 149 |
|
|
|
|
| 151 |
|
| 152 |
| Model | Format | Resolution | Top 1 Accuracy |
|
| 153 |
|-------|--------|------------|----------------|
|
| 154 |
+
| [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 % |
|
| 155 |
+
| [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 % |
|
| 156 |
+
| [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 % |
|
| 157 |
+
| [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 % |
|
| 158 |
+
| [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 % |
|
| 159 |
+
| [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 % |
|
| 160 |
+
| [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 % |
|
| 161 |
+
| [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 % |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 162 |
|
| 163 |
|
| 164 |
|
|
|
|
| 169 |
|
| 170 |
| Model | Format | Resolution | Top 1 Accuracy |
|
| 171 |
|-------|--------|------------|----------------|
|
| 172 |
+
| [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 % |
|
| 173 |
+
| [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 % |
|
| 174 |
+
| [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 % |
|
| 175 |
+
| [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 % |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
| 176 |
|
| 177 |
|
| 178 |
### Accuracy with Food-101 dataset
|
|
|
|
| 182 |
|
| 183 |
| Model | Format | Resolution | Top 1 Accuracy |
|
| 184 |
|-------|--------|------------|----------------|
|
| 185 |
+
| [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 % |
|
| 186 |
+
| [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 % |
|
| 187 |
+
| [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 % |
|
| 188 |
+
| [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 % |
|
| 189 |
+
| [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 % |
|
| 190 |
+
| [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 % |
|
| 191 |
+
|
| 192 |
+
|
| 193 |
+
### Accuracy with imagenet dataset
|
|
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|
| 194 |
|
| 195 |
Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4).
|
| 196 |
Number of classes: 1000.
|
|
|
|
| 199 |
|
| 200 |
|model | Format | Resolution | Top 1 Accuracy |
|
| 201 |
|---------|--------|------------|----------------|
|
| 202 |
+
| [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 % |
|
| 203 |
+
| [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 % |
|
| 204 |
+
| [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 % |
|
| 205 |
+
| [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 % |
|
| 206 |
+
| [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 % |
|
| 207 |
+
| [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 % |
|
| 208 |
+
| [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 % |
|
| 209 |
|
| 210 |
|
| 211 |
|