| # Overview of PyTorch STM32 Model Zoo for Object Detection |
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| The STM32 model zoo includes several PyTorch-based models for object detection use cases, converted to ONNX format and optimized for STM32N6 NPU deployment. All models are pre-trained on ImageNet and quantized using QDQ INT8 quantization. |
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| ## Model Categories |
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| - `ST_pretrainedmodel_public_dataset` contains PyTorch object detection models pretrained by ST on open source datasets such as MS COCO, Pascal VOC, COCO person and converted to ONNX format with QDQ quantization. |
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| **Explore the complete PyTorch model zoo with pre-trained models optimized for STM32N6.** |
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| ## Model Families |
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| You can get comprehensive footprints and performance information for each model family following the links below: |
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| ### Single Shot Detector (SSD) Models |
| Variation of SSD models using different backbone and heads |
| - [ssd_mobilenetv1_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/ssd_mobilenetv1_pt/README.md) – Single shot detector with MobilenetV1 backbone |
| - [ssd_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/ssd_mobilenetv2_pt/README.md) – Single shot detector with MobilenetV2 backbone |
| - [ssdlite_mobilenetv1_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/ssdlite_mobilenetv1_pt/README.md) – Single shot detector lite with MobilenetV1 backbone |
| - [ssdlite_mobilenetv2_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/ssdlite_mobilenetv2_pt/README.md) – Single shot detector lite with MobilenetV2 backbone |
| - [ssdlite_mobilenetv3mall_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/ssdlite_mobilenetv3mall_pt/README.md) – Single shot detector lite with MobilenetV3small backbone |
| - [ssdlite_mobilenetv3large_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/ssdlite_mobilenetv3large_pt/README.md) – Single shot detector lite with MobilenetV3large backbone |
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| ### ST_YOLOD Models |
| STMicroelectronics in house developed model especially optimized for size and memory with a competitive performance on Imagenet (and other datasets) |
| - [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/st_yolodv2milli_pt/README.md) – Extreme compression of STResNet for backbone and YOLOX head and modified YOLOX neck. |
| - [st_yolodv2tiny_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/master/object_classification/st_yolodv2tiny_pt/README.md) – Extreme compression of STResNet for backbone and YOLOX head and modified YOLOX neck. |
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| **Feel free to explore the model zoo and get pre-trained models [here](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/master/object_detection/).** |
| For training and deployment guidance, refer to the STM32 AI model zoo documentation. |
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