| --- |
| license: other |
| license_name: sla0044 |
| license_link: >- |
| https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/LICENSE.md |
| pipeline_tag: object-detection |
| --- |
| # **SSD MobileNetV2** |
|
|
| ## **Use case** : `Object detection` |
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| ## **Model description** |
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| SSD MobileNetV2 is a **single-shot object detection model** designed for **efficient and low-latency inference** on resource-constrained devices such as mobile and edge platforms. |
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| The model combines the **Single Shot Detector (SSD)** framework with **MobileNetV2** as the backbone network. MobileNetV2 employs **inverted residual blocks and linear bottlenecks**, enabling a strong balance between accuracy and computational efficiency. |
| The SSD head performs object localization and classification in a **single forward pass**, making the model suitable for real-time detection scenarios. |
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| The `ssd_mobilenetv2_pt` variant is implemented in **PyTorch** and is commonly used as a lightweight baseline for object detection tasks where **speed, memory footprint, and power efficiency** are critical. |
|
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| ## **Network information** |
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| | Network information | Value | |
| |--------------------|-------| |
| | Framework | Torch | |
| | Quantization | Int8 | |
| | Provenance | [torchvision GitHub](https://github.com/pytorch/vision) | |
| | Paper | [SSD](https://arxiv.org/abs/1512.02325)<br>[MobileNetV2](https://arxiv.org/abs/1801.04381) | |
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| The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment. |
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| ## Network inputs / outputs |
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| For an image resolution of NxM and NC classes |
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| | Input Shape | Description | |
| | ----- | ----------- | |
| | (1, W, H, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
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| | Output Shape | Description | |
| | ----- | ----------- | |
| | (1, 3000,(1+NC) and (1,3000,4)) | Model returns two output vectors of bounding boxes where first output returns confidence for each class (+ background class) and second output returns bounding box coordinates (x1, y1, x2,y2) | |
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|
|
| ## Recommended Platforms |
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| | Platform | Supported | Recommended | |
| |----------|-----------|-------------| |
| | STM32L0 | [] | [] | |
| | STM32L4 | [] | [] | |
| | STM32U5 | [] | [] | |
| | STM32H7 | [] | [] | |
| | STM32MP1 | [] | [] | |
| | STM32MP2 | [] | [] | |
| | STM32N6 | [x] | [x] | |
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| # Performances |
|
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| ## Metrics |
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| Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option. |
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| ### Reference **NPU** memory footprint based on COCO dataset (see Accuracy for details on dataset) |
| | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |
| |-------|---------|--------|------------|--------|-------------------|-------------------|--------------------|-----------------------| |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300_qdq_int8.onnx) | COCO | Int8 | 300x300x3 | STM32N6 | 2323.25 | 2109.38 | 20033.69 | 3.0.0 | |
|
|
| ### Reference **NPU** inference time based on COCO dataset (see Accuracy for details on dataset) |
| | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| |-------|---------|--------|------------|-------|------------------|--------------------|-----------|-----------------------| |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300_qdq_int8.onnx) | COCO | Int8 | 300x300x3 | STM32N6570-DK | NPU/MCU | 158.49 | 6.31 | 3.0.0 | |
|
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| ### 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) | STEdgeAI Core version | |
| |-------|---------|--------|------------|--------|-------------------|-------------------|--------------------|-----------------------| |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300_qdq_int8.onnx) | COCO-Person | Int8 | 300x300x3 | STM32N6 | 2182.72 | 2109.38 | 8005.94 | 3.0.0 | |
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| ### 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 | STEdgeAI Core version | |
| |-------|---------|--------|------------|-------|------------------|--------------------|-----------|-----------------------| |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300_qdq_int8.onnx) | COCO-Person | Int8 | 300x300x3 | STM32N6570-DK | NPU/MCU | 126.19 | 7.92 | 3.0.0 | |
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| ### Reference **NPU** memory footprint based on VOC dataset (see Accuracy for details on dataset) |
| | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |
| |-------|---------|--------|------------|--------|-------------------|-------------------|--------------------|-----------------------| |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300_qdq_int8.onnx) | VOC | Int8 | 300x300x3 | STM32N6 | 2237.00 | 2109.38 | 10898.69 | 3.0.0 | |
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| ### Reference **NPU** inference time based on VOC dataset (see Accuracy for details on dataset) |
| | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| |-------|---------|--------|------------|-------|------------------|--------------------|-----------|-----------------------| |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300_qdq_int8.onnx) | VOC | Int8 | 300x300x3 | STM32N6570-DK | NPU/MCU | 135.06 | 7.40 | 3.0.0 | |
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|
|
| ### AP on COCO dataset |
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| Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode), Number of classes: 80 |
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| | Model | Format | Resolution | AP50 | |
| | --- | --- | --- | --- | |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300.onnx) | Float | 3x300x300 | 31.75 | |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/coco/ssd_mobilenetv2_pt_coco_300/ssd_mobilenetv2_pt_coco_300_qdq_int8.onnx) | Int8 | 3x300x300 | 31.29 | |
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| \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100 |
|
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| ### AP on COCO-Person dataset |
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| Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) , Number of classes: 1 |
|
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| | Model | Format | Resolution | AP50 | |
| | --- | --- | --- | --- | |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300.onnx) | Float | 3x300x300 | 41.91 | |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/ST_pretrainedmodel_public_dataset/coco_person/ssd_mobilenetv2_pt_coco_person_300/ssd_mobilenetv2_pt_coco_person_300_qdq_int8.onnx) | Int8 | 3x300x300 | 41.74 | |
|
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| \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100 |
|
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| ### AP on VOC dataset |
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| Dataset details: [link](https://www.robots.ox.ac.uk/~vgg/projects/pascal/VOC/) , [License](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/HTML/license.html) , Number of classes: 20 |
|
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| | Model | Format | Resolution | AP50 | |
| | --- | --- | --- | --- | |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300.onnx) | Float | 3x300x300 | 67.03 | |
| | [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/ssd_mobilenetv2_pt/Public_pretrainedmodel_public_dataset/voc/ssd_mobilenetv2_pt_voc_300/ssd_mobilenetv2_pt_voc_300_qdq_int8.onnx) | Int8 | 3x300x300 | 66.91 | |
|
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| \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100 |
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|
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| ## Retraining and Integration in a simple example: |
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| Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) |
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|
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| ## Datasets |
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| - **COCO** |
| [Lin et al., *Microsoft COCO: Common Objects in Context*](https://arxiv.org/abs/1405.0312) |
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| - **PASCAL VOC** |
| [Everingham et al., *The PASCAL Visual Object Classes (VOC) Challenge*](http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.pdf) |
|
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| ## References |
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| - **SSD** |
| [Liu et al., *SSD: Single Shot MultiBox Detector*](https://arxiv.org/abs/1512.02325) |
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| - **MobileNetV2** |
| [Sandler et al., *MobileNetV2: Inverted Residuals and Linear Bottlenecks*](https://arxiv.org/abs/1801.04381) |
