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Update ST Model Zoo

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@@ -1,9 +1,3 @@
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- ---
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- license: other
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- license_name: sla0044
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- license_link: >-
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- https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/LICENSE.md
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- ---
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  # Fd-MobileNet
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  ## **Use case** : `Image classification`
@@ -65,60 +59,59 @@ For an image resolution of NxM and P classes and 0.25 alpha parameter :
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  * Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
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  * `tfs` stands for "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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68
-
69
  ### Reference **NPU** memory footprint on food-101 dataset (see Accuracy for details on dataset)
70
- |Model | Format | Resolution | Series | Internal RAM (KiB)| External RAM (KiB)| Weights Flash (KiB)| STM32Cube.AI version | STEdgeAI Core version |
71
- |----------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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- | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_224_tfs/fdmobilenet_0.25_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6 | 294 |0.0| 209.92 | 10.0.0 | 2.0.0 |
73
- | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_224_tfs/st_fdmobilenet_v1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6 | 294 | 0.0 | 236.49 | 10.0.0 | 2.0.0 |
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- | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_128_tfs/fdmobilenet_0.25_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6 | 96 | 0.0 | 209.92 | 10.0.0 | 2.0.0 |
75
- | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_128_tfs/st_fdmobilenet_v1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6 | 96 | 0.0 | 236.49 | 10.0.0 | 2.0.0 |
76
 
77
 
78
  ### Reference **NPU** inference time on food-101 dataset (see Accuracy for details on dataset)
79
  | Model | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
80
  |--------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
81
- | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_224_tfs/fdmobilenet_0.25_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 1.46 | 684.93 | 10.0.0 | 2.0.0 |
82
- | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_224_tfs/st_fdmobilenet_v1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 1.81 | 552.49 | 10.0.0 | 2.0.0 |
83
- | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_128_tfs/fdmobilenet_0.25_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 0.93 | 1075.27 | 10.0.0 | 2.0.0 |
84
- | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_128_tfs/st_fdmobilenet_v1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 1.07 | 934.58 | 10.0.0 | 2.0.0 |
85
 
86
 
87
  ### Reference **MCU** memory footprints based on Flowers dataset (see Accuracy for details on dataset)
88
  | Model | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
89
  |-----------------------|--------|--------------|---------|----------------|-------------|---------------|------------|------------|-------------|----------------------|
90
- | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | STM32H7 | 157.03 KiB | 14.25 KiB | 128.32 KiB | 58.66 KiB | 171.28 KiB | 186.98 KiB | 10.0.0 |
91
- | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | STM32H7 | 211.64 KiB | 14.25 KiB | 144.93 KiB | 60.17 KiB | 225.89 KiB | 205.1 KiB | 10.0.0 |
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- | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | STM32H7 | 56.16 KiB | 14.2 KiB | 128.32 KiB | 58.16 KiB | 70.36 KiB | 186.95 KiB | 10.0.0 |
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- | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | STM32H7 | 74.23 KiB | 14.2 KiB | 144.93 KiB | 60.12 KiB | 88.43 KiB | 205.05 KiB | 10.0.0 |
94
 
95
 
96
  ### Reference **MCU** inference time based on Flowers dataset (see Accuracy for details on dataset)
97
  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
98
  |-----------------------|--------|--------------|------------------|------------------|---------------|---------------------|----------------------|
99
- | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 53.52 ms | 10.0.0 |
100
- | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 102 ms | 10.0.0 |
101
- | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 17.73 ms | 10.0.0 |
102
- | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 32.14 ms | 10.0.0 |
103
- | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | STM32F769I-DISCO | 1 CPU | 216 MHz | 176.5 ms | 10.0.0 |
104
- | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | STM32F769I-DISCO | 1 CPU | 216 MHz | 59.29 ms | 10.0.0 |
105
 
106
 
107
  ### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset)
108
  | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
109
  |-----------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
110
- | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 6.60 ms | 12.28 | 87.72 | 0 | v5.1.0 | OpenVX |
111
- | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 7.84 ms | 10.82 | 89.19 | 0 | v5.1.0 | OpenVX |
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- | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 2.17 ms | 15.66 | 84.34 | 0 | v5.1.0 | OpenVX |
113
- | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 2.85 ms | 12.75 | 87.25 | 0 | v5.1.0 | OpenVX |
114
- | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 22.76 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
115
- | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 33.93 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
116
- | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 8.08 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
117
- | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 13.16 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
118
- | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 33.50 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
119
- | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 61.00 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
120
- | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 10.86 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
121
- | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 19.43 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
122
 
123
  ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
124
 
@@ -164,9 +157,9 @@ Number of classes: 101, number of files: 101000
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  | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | 58.78 |
165
  | ST FdMobileNet v1 tfs | Float | 224x224x3 | 66.19 |
166
  | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | 64.71 |
167
- | FdMobileNet 0.25 tfs | Float | 128x128x3 | 45.54 |
168
  | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | 44.86 |
169
- | ST FdMobileNet v1 tfs | Float | 128x128x3 | 54.19 |
170
  | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | 53.74 |
171
 
172
 
@@ -184,5 +177,4 @@ Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/S
184
  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
185
 
186
  <a id="3">[3]</a>
187
- L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014.
188
-
 
 
 
 
 
 
 
1
  # Fd-MobileNet
2
 
3
  ## **Use case** : `Image classification`
 
59
  * Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
60
  * `tfs` 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.
61
 
 
62
  ### Reference **NPU** memory footprint on food-101 dataset (see Accuracy for details on dataset)
63
+ |Model | Format | Resolution | Series | Internal RAM (KiB)| External RAM (KiB)| Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
64
+ |----------|--------|-------------|------------------|------------------|---------------------|---------------------|----------------------|-------------------------|
65
+ | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_224_tfs/fdmobilenet_0.25_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6 | 294 |0.0| 197.56 | 10.2.0 | 2.2.0 |
66
+ | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_224_tfs/st_fdmobilenet_v1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6 | 294 | 0.0 | 222.33 | 10.2.0 | 2.2.0 |
67
+ | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_128_tfs/fdmobilenet_0.25_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6 | 96 | 0.0 | 197.56 | 10.2.0 | 2.2.0 |
68
+ | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_128_tfs/st_fdmobilenet_v1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6 | 96 | 0.0 | 222.33 | 10.2.0 | 2.2.0 |
69
 
70
 
71
  ### Reference **NPU** inference time on food-101 dataset (see Accuracy for details on dataset)
72
  | Model | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
73
  |--------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
74
+ | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_224_tfs/fdmobilenet_0.25_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 1.46 | 684.93 | 10.2.0 | 2.2.0 |
75
+ | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_224_tfs/st_fdmobilenet_v1_224_tfs_int8.tflite) | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 1.81 | 552.49 | 10.2.0 | 2.2.0 |
76
+ | [FdMobileNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/fdmobilenet_0.25_128_tfs/fdmobilenet_0.25_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 0.93 | 1075.27 | 10.2.0 | 2.2.0 |
77
+ | [ST FdMobileNet v1 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/fdmobilenet/ST_pretrainedmodel_public_dataset/food-101/st_fdmobilenet_v1_128_tfs/st_fdmobilenet_v1_128_tfs_int8.tflite) | Int8 | 128x128x3 | STM32N6570-DK | NPU/MCU | 1.07 | 934.58 | 10.2.0 | 2.2.0 |
78
 
79
 
80
  ### Reference **MCU** memory footprints based on Flowers dataset (see Accuracy for details on dataset)
81
  | Model | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
82
  |-----------------------|--------|--------------|---------|----------------|-------------|---------------|------------|------------|-------------|----------------------|
83
+ | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | STM32H7 | 157.03 KiB | 14.25 KiB | 128.32 KiB | 57.01 KiB | 171.28 KiB | 185.33 KiB | 10.2.0 |
84
+ | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | STM32H7 | 211.64 KiB | 14.25 KiB | 144.93 KiB | 58.51 KiB | 225.89 KiB | 203.44 KiB | 10.2.0 |
85
+ | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | STM32H7 | 56.16 KiB | 14.2 KiB | 128.32 KiB | 56.98 KiB | 70.36 KiB | 185.3 KiB | 10.2.0 |
86
+ | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | STM32H7 | 74.23 KiB | 14.2 KiB | 144.93 KiB | 58.47 KiB | 88.43 KiB | 203.4 KiB | 10.2.0 |
87
 
88
 
89
  ### Reference **MCU** inference time based on Flowers dataset (see Accuracy for details on dataset)
90
  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
91
  |-----------------------|--------|--------------|------------------|------------------|---------------|---------------------|----------------------|
92
+ | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 54.36 ms | 10.2.0 |
93
+ | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 103.45 ms | 10.2.0 |
94
+ | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 18.04 ms | 10.2.0 |
95
+ | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 32.69 ms | 10.2.0 |
96
+ | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | STM32F769I-DISCO | 1 CPU | 216 MHz | 176.5 ms | 10.2.0 |
97
+ | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | STM32F769I-DISCO | 1 CPU | 216 MHz | 59.29 ms | 10.2.0 |
98
 
99
 
100
  ### Reference **MPU** inference time based on Flowers dataset (see Accuracy for details on dataset)
101
  | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
102
  |-----------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
103
+ | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 6.59 ms | 13.71 | 86.29 | 0 | v6.1.0 | OpenVX |
104
+ | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 7.79 ms | 11.59 | 88.41 | 0 | v6.1.0 | OpenVX |
105
+ | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 2.23 ms | 19.22 | 80.78 | 0 | v6.1.0 | OpenVX |
106
+ | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 2.84 ms | 18.90 | 81.10 | 0 | v6.1.0 | OpenVX |
107
+ | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 22.87 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
108
+ | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 39.05 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
109
+ | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 7.98 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
110
+ | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 13.54 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
111
+ | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 33.72 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
112
+ | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 60.14 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
113
+ | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 10.88 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
114
+ | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 19.59 ms | NA | NA | 100 | v6.1.0 | TensorFlowLite 2.18.0 |
115
 
116
  ** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**
117
 
 
157
  | FdMobileNet 0.25 tfs | Int8 | 224x224x3 | 58.78 |
158
  | ST FdMobileNet v1 tfs | Float | 224x224x3 | 66.19 |
159
  | ST FdMobileNet v1 tfs | Int8 | 224x224x3 | 64.71 |
160
+ | FdMobileNet 0.25 tfs | Float | 128x128x3 | 45.58 |
161
  | FdMobileNet 0.25 tfs | Int8 | 128x128x3 | 44.86 |
162
+ | ST FdMobileNet v1 tfs | Float | 128x128x3 | 54.22 |
163
  | ST FdMobileNet v1 tfs | Int8 | 128x128x3 | 53.74 |
164
 
165
 
 
177
  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
178
 
179
  <a id="3">[3]</a>
180
+ L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 -- Mining Discriminative Components with Random Forests." European Conference on Computer Vision, 2014.