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ssd_mobilenet_v2_fpnlite

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@@ -69,30 +69,30 @@ Measures are done with default STM32Cube.AI configuration with enabled input / o
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  ### Reference **NPU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
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  |Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
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  |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | COCO-Person | Int8 | 192x192x3 | STM32N6 | 606.49 | 0.0 | 1580.53 | 10.0.0 | 2.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | COCO-Person | Int8 | 224x224x3 | STM32N6 | 1314.67 | 0.0 | 1607.41 | 10.0.0 | 2.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6 | 1959.06 | 0.0 | 1637.02 | 10.0.0 | 2.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | COCO-Person | Int8 | 416x416x3 | STM32N6 | 4570.03 | 0.0 | 1837.8 | 10.0.0 | 2.0.0 |
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  ### Reference **NPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
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  | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
81
  |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | COCO-Person | Int8 | 192x192x3 | STM32N6570-DK | NPU/MCU | 14.37 | 69.57 | 10.0.0 | 2.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | COCO-Person | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 18.15 | 55.10 | 10.0.0 | 2.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 21.73 | 46.03 | 10.0.0 | 2.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | COCO-Person | Int8 | 416x416x3 | STM32N6570-DK | NPU/MCU | 114.12 | 8.76 | 10.0.0 | 2.0.0 |
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  ### Reference **MCU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
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  | Model | Format | Resolution | Series | Activation RAM (KiB) | Runtime RAM (KiB) | Weights Flash (KiB) | Code Flash (KiB) | Total RAM (KiB) | Total Flash (KiB) | STM32Cube.AI version |
91
  |-------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------|
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | STM32H7 | 521.210.0.0 | 70.26 | 1098.76 | 192.69 | 591.46 | 1291.45 | 10.0.0 | |
93
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 956.82 | 70.3 | 1120.63 | 192.84 | 1027.12 | 1313.47 | 10.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | STM32H7 | 1238.29 | 70.3 | 1145.24 | 192.81 | 1308.59 | 1338.05 | 10.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | STM32H7 | 2869.05 | 70.3 | 1321.02 | 193.23 | 2939.35 | 1514.25 | 10.0.0 |
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  ### Reference **MCU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
@@ -100,40 +100,40 @@ Measures are done with default STM32Cube.AI configuration with enabled input / o
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  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
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  |-------------------|--------|------------|------------------|------------------|-------------|---------------------|-----------------------|
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 511.16 ms | 10.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 673.19 ms | 10.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 898.32 ms | 10.0.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 2684.93 ms | 10.0.0 |
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  ### Reference **MPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
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  | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
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  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 35.08 ms | 6.20 | 93.80 |0 | v5.1.0 | OpenVX |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 48.92 ms | 6.19 | 93.81 |0 | v5.1.0 | OpenVX |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 40.66 ms | 7.07 | 92.93 |0 | v5.1.0 | OpenVX |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 110.4 ms | 4.47 | 95.53 |0 | v5.1.0 | OpenVX |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 193.70 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
118
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 263.60 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
119
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 339.40 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
120
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 894.00 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
121
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 287.40 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 383.40 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
123
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 498.90 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 1348.00 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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127
  ### Reference **MPU** inference time based on COCO 80 classes dataset (see Accuracy for details on dataset)
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  | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
130
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
131
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 100.90 ms | 8.86 | 91.14 |0 | v5.1.0 | OpenVX |
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- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 280.00 ms | 8.68 | 91.32 |0 | v5.1.0 | OpenVX |
133
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 742.90 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
134
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 2000 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
135
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 1112.00 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 2986 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
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@@ -146,14 +146,14 @@ Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0]
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  | Model | Format | Resolution | AP* |
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  |-------|--------|------------|----------------|
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | 40.7 % |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192.h5) | Float | 192x192x3 | 40.8 % |
151
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | 51.1 % |
152
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224.h5) | Float | 224x224x3 | 51.7 % |
153
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | 58.3 % |
154
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256.h5) | Float | 256x256x3 | 58.8 % |
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- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | 61.9 % |
156
- | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416.h5) | Float | 416x416x3 | 62.6 % |
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  \* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
@@ -166,10 +166,10 @@ Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0]
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  | Model | Format | Resolution | AP* |
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  |-------|--------|------------|----------------|
169
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | 32.2 % |
170
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256.h5) | Float | 256x256x3 | 32.6 % |
171
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | 32.3 % |
172
- | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416.h5) | Float | 416x416x3 | 34.8 % |
173
 
174
  \* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
175
 
 
69
  ### Reference **NPU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
70
  |Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
71
  |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
72
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | COCO-Person | Int8 | 192x192x3 | STM32N6 | 606.49 | 0.0 | 1580.53 | 10.0.0 | 2.0.0 |
73
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | COCO-Person | Int8 | 224x224x3 | STM32N6 | 1314.67 | 0.0 | 1607.41 | 10.0.0 | 2.0.0 |
74
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6 | 1959.06 | 0.0 | 1637.02 | 10.0.0 | 2.0.0 |
75
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | COCO-Person | Int8 | 416x416x3 | STM32N6 | 4570.03 | 0.0 | 1837.8 | 10.0.0 | 2.0.0 |
76
 
77
  ### Reference **NPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
78
 
79
 
80
  | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
81
  |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
82
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | COCO-Person | Int8 | 192x192x3 | STM32N6570-DK | NPU/MCU | 14.37 | 69.57 | 10.0.0 | 2.0.0 |
83
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | COCO-Person | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 18.15 | 55.10 | 10.0.0 | 2.0.0 |
84
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 21.73 | 46.03 | 10.0.0 | 2.0.0 |
85
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | COCO-Person | Int8 | 416x416x3 | STM32N6570-DK | NPU/MCU | 114.12 | 8.76 | 10.0.0 | 2.0.0 |
86
 
87
  ### Reference **MCU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
88
 
89
 
90
  | Model | Format | Resolution | Series | Activation RAM (KiB) | Runtime RAM (KiB) | Weights Flash (KiB) | Code Flash (KiB) | Total RAM (KiB) | Total Flash (KiB) | STM32Cube.AI version |
91
  |-------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------|
92
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | STM32H7 | 521.210.0.0 | 70.26 | 1098.76 | 192.69 | 591.46 | 1291.45 | 10.0.0 | |
93
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | STM32H7 | 956.82 | 70.3 | 1120.63 | 192.84 | 1027.12 | 1313.47 | 10.0.0 |
94
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | STM32H7 | 1238.29 | 70.3 | 1145.24 | 192.81 | 1308.59 | 1338.05 | 10.0.0 |
95
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | STM32H7 | 2869.05 | 70.3 | 1321.02 | 193.23 | 2939.35 | 1514.25 | 10.0.0 |
96
 
97
 
98
  ### Reference **MCU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
 
100
 
101
  | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
102
  |-------------------|--------|------------|------------------|------------------|-------------|---------------------|-----------------------|
103
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 511.16 ms | 10.0.0 |
104
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 673.19 ms | 10.0.0 |
105
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 898.32 ms | 10.0.0 |
106
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 2684.93 ms | 10.0.0 |
107
 
108
 
109
  ### Reference **MPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
110
 
111
  | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
112
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
113
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 35.08 ms | 6.20 | 93.80 |0 | v5.1.0 | OpenVX |
114
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 48.92 ms | 6.19 | 93.81 |0 | v5.1.0 | OpenVX |
115
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 40.66 ms | 7.07 | 92.93 |0 | v5.1.0 | OpenVX |
116
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 110.4 ms | 4.47 | 95.53 |0 | v5.1.0 | OpenVX |
117
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 193.70 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
118
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 263.60 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
119
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 339.40 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
120
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 894.00 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
121
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 287.40 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
122
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 383.40 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
123
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 498.90 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
124
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 1348.00 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
125
 
126
 
127
  ### Reference **MPU** inference time based on COCO 80 classes dataset (see Accuracy for details on dataset)
128
 
129
  | Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
130
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
131
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 100.90 ms | 8.86 | 91.14 |0 | v5.1.0 | OpenVX |
132
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 280.00 ms | 8.68 | 91.32 |0 | v5.1.0 | OpenVX |
133
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 742.90 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
134
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 2000 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
135
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 1112.00 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
136
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 2986 ms | NA | NA |100 | v5.1.0 | TensorFlowLite 2.11.0 |
137
 
138
 
139
 
 
146
 
147
  | Model | Format | Resolution | AP* |
148
  |-------|--------|------------|----------------|
149
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192_int8.tflite) | Int8 | 192x192x3 | 40.7 % |
150
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_192/ssd_mobilenet_v2_fpnlite_035_192.h5) | Float | 192x192x3 | 40.8 % |
151
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224_int8.tflite) | Int8 | 224x224x3 | 51.1 % |
152
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_224/ssd_mobilenet_v2_fpnlite_035_224.h5) | Float | 224x224x3 | 51.7 % |
153
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256_int8.tflite) | Int8 | 256x256x3 | 58.3 % |
154
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_256/ssd_mobilenet_v2_fpnlite_035_256.h5) | Float | 256x256x3 | 58.8 % |
155
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416_int8.tflite) | Int8 | 416x416x3 | 61.9 % |
156
+ | [SSD Mobilenet v2 0.35 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_person/ssd_mobilenet_v2_fpnlite_035_416/ssd_mobilenet_v2_fpnlite_035_416.h5) | Float | 416x416x3 | 62.6 % |
157
 
158
 
159
  \* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
 
166
 
167
  | Model | Format | Resolution | AP* |
168
  |-------|--------|------------|----------------|
169
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256_int8.tflite) | Int8 | 256x256x3 | 32.2 % |
170
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_256/ssd_mobilenet_v2_fpnlite_100_256.h5) | Float | 256x256x3 | 32.6 % |
171
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416_int8.tflite) | Int8 | 416x416x3 | 32.3 % |
172
+ | [SSD Mobilenet v2 1.0 FPN-lite](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/coco_2017_80_classes/ssd_mobilenet_v2_fpnlite_100_416/ssd_mobilenet_v2_fpnlite_100_416.h5) | Float | 416x416x3 | 34.8 % |
173
 
174
  \* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
175