|
--- |
|
license: other |
|
license_name: sla0044 |
|
license_link: >- |
|
https://github.com/STMicroelectronics/stm32aimodelzoo/object_detection/ssd_mobilenet_v2_fpnlite/ST_pretrainedmodel_public_dataset/LICENSE.md |
|
pipeline_tag: object-detection |
|
--- |
|
# SSD MobileNet v2 FPN-lite quantized |
|
|
|
## **Use case** : `Object detection` |
|
|
|
# Model description |
|
|
|
|
|
The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. |
|
Mobilenet-ssd is using MobileNetV2 as a backbone which is a general architecture that can be used for multiple use cases. |
|
Depending on the use case, it can use different input layer size and |
|
different width factors. This allows different width models to reduce |
|
the number of multiply-adds and thereby reduce inference cost on mobile devices. |
|
|
|
The model is quantized in int8 using tensorflow lite converter. |
|
|
|
## Network information |
|
|
|
|
|
| Network information | Value | |
|
|-------------------------|-----------------| |
|
| Framework | TensorFlow Lite | |
|
| Quantization | int8 | |
|
| Provenance | https://www.tensorflow.org/api_docs/python/tf/keras/applications/mobilenet_v2 | |
|
| Paper | https://arxiv.org/abs/1801.04381, https://arxiv.org/abs/1512.02325 | |
|
|
|
The models are quantized using tensorflow lite converter. |
|
|
|
|
|
## Network inputs / outputs |
|
|
|
|
|
For an image resolution of NxM and NC classes |
|
|
|
| Input Shape | Description | |
|
| ----- | ----------- | |
|
| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
|
|
|
| Output Shape | Description | |
|
| ----- | ----------- | |
|
| (1, NA, 8 + NC) | FLOAT values Where NA is thge number of anchors and NC is the number of classes| |
|
|
|
|
|
## Recommended Platforms |
|
|
|
|
|
| Platform | Supported | Recommended | |
|
|----------|-----------|-------------| |
|
| STM32L0 | [] | [] | |
|
| STM32L4 | [] | [] | |
|
| STM32U5 | [] | [] | |
|
| STM32H7 | [x] | [x] | |
|
| STM32MP1 | [x] | [x] | |
|
| STM32MP2 | [x] | [x] | |
|
| STM32N6 | [x] | [x] | |
|
|
|
# Performances |
|
|
|
## Metrics |
|
|
|
Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option. |
|
|
|
### Reference **NPU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset) |
|
|Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version | |
|
|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------| |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
|
|
### Reference **NPU** inference time based on COCO Person dataset (see Accuracy for details on dataset) |
|
|
|
|
|
| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version | |
|
|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------| |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
|
|
### Reference **MCU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset) |
|
|
|
|
|
| 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 | |
|
|-------------------|--------|------------|---------|----------------|-------------|---------------|------------|-------------|-------------|-----------------------| |
|
| [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 | | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
|
|
|
|
### Reference **MCU** inference time based on COCO Person dataset (see Accuracy for details on dataset) |
|
|
|
|
|
| Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version | |
|
|-------------------|--------|------------|------------------|------------------|-------------|---------------------|-----------------------| |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
|
|
|
|
### Reference **MPU** inference time based on COCO Person dataset (see Accuracy for details on dataset) |
|
|
|
| Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework | |
|
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------| |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
|
|
|
|
### Reference **MPU** inference time based on COCO 80 classes dataset (see Accuracy for details on dataset) |
|
|
|
| Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework | |
|
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------| |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
|
|
|
|
|
|
** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization** |
|
|
|
### AP on COCO Person dataset |
|
|
|
|
|
Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) , Quotation[[1]](#1) , Number of classes: 80, Number of images: 118,287 |
|
|
|
| Model | Format | Resolution | AP* | |
|
|-------|--------|------------|----------------| |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
|
|
|
|
\* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001 |
|
|
|
|
|
### AP on COCO 80 classes dataset |
|
|
|
|
|
Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) , Quotation[[1]](#1) , Number of classes: 80, Number of images: 118,287 |
|
|
|
| Model | Format | Resolution | AP* | |
|
|-------|--------|------------|----------------| |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
| [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 % | |
|
|
|
\* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001 |
|
|
|
## Retraining and Integration in a simple example: |
|
|
|
Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) |
|
|
|
|
|
|
|
# References |
|
|
|
|
|
<a id="1">[1]</a> |
|
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014. "Microsoft coco: Common objects in context". In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 (pp. 740-755). Springer International Publishing. [Online]. Available: https://cocodataset.org/#download. |
