st_yolodv2milli_pt / README.md

Release AI-ModelZoo-4.0.0

cde7157 verified 10 days ago

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	---
	license: other
	license_name: sla0044
	license_link: >-
	https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/LICENSE.md
	pipeline_tag: object-detection
	---
	# STYOLOMilli

	## Use case : `Object detection`

	## Model description

	STYOLOMilli is a compact and efficient object detection model designed for deployment on resource‑constrained hardware such as microcontroller units (MCUs) and neural processing units (NPUs). It is part of the STYOLO family introduced alongside STResNet in the paper [STResNet & STYOLO: A New Family of Compact Classification and Object Detection Models for MCUs](https://arxiv.org/abs/2601.05364).

	The model builds on a highly compressed backbone derived from the STResNet design and integrates it within a YOLOX‑style architecture optimized for low memory usage, high efficiency, and competitive accuracy. STYOLOMilli delivers strong object detection performance (33.6 mAP on MS COCO) while maintaining a very small model footprint, outperforming or matching other micro sized detectors such as YOLOv5n and YOLOX‑Nano on the same tasks.

	The `st_yolo_milli_pt` variant is implemented in PyTorch and is tuned for ultra‑efficient inference on edge and MCU environments.

	## Network information

	\| Network information \| Value \|
	\|--------------------\|-------\|
	\| Framework \| Torch \|
	\| Quantization \| Int8 \|
	\| Provenance \| [STMicroelectronics Model Zoo Services](https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main/object_detection/object_detection) \|
	\| Paper \| [STResNet & STYOLO (arXiv:2601.05364)](https://arxiv.org/abs/2601.05364) \|

	The model is quantized to int8 using ONNX Runtime and exported for efficient deployment.


	## Network inputs / outputs

	For an image resolution of NxM and NC classes

	\| Input Shape \| Description \|
	\| ----- \| ----------- \|
	\| (1, W, H, 3) \| Single NxM RGB image with UINT8 values between 0 and 255 \|

	\| Output Shape \| Description \|
	\| ----- \| ----------- \|
	\| (1, (W/8xH/8 + W/16xH/16 + W/32xH/32), (NC+1+4)) \| Model returns bounding boxes with 6 values for each box, four coordinates (x1,y1,x2,y2), class confidence and objectness confidence \|


	## Recommended Platforms

	\| Platform \| Supported \| Recommended \|
	\|----------\|-----------\|-------------\|
	\| STM32L0 \| [] \| [] \|
	\| STM32L4 \| [] \| [] \|
	\| STM32U5 \| [] \| [] \|
	\| STM32H7 \| [] \| [] \|
	\| STM32MP1 \| [] \| [] \|
	\| STM32MP2 \| [] \| [] \|
	\| STM32N6 \| [x] \| [x] \|


	# Performances

	## Metrics

	Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option.

	### 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 \|
	\|-------\|---------\|--------\|------------\|--------\|-------------------\|-------------------\|--------------------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192_qdq_int8.onnx) \| COCO \| Int8 \| 192x192x3 \| STM32N6 \| 1008.00 \| 0 \| 3170.29 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320_qdq_int8.onnx) \| COCO \| Int8 \| 320x320x3 \| STM32N6 \| 2744.48 \| 800.00 \| 3182.67 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640_qdq_int8.onnx) \| COCO \| Int8 \| 640x640x3 \| STM32N6 \| 2768.00 \| 9600.00 \| 3189.38 \| 3.0.0 \|

	* 640x640 coco checkpoints are provided primarily for finetuning purposes as these checkpoints are trained on large dataset at higher resolution. Models with 640 resolution is not suitable for deployment.

	### 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 \|
	\|-------\|---------\|--------\|------------\|-------\|------------------\|--------------------\|-----------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192_qdq_int8.onnx) \| COCO \| Int8 \| 192x192x3 \| STM32N6570-DK \| NPU/MCU \| 18.78 \| 53.25 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320_qdq_int8.onnx) \| COCO \| Int8 \| 320x320x3 \| STM32N6570-DK \| NPU/MCU \| 70.57 \| 14.17 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640_qdq_int8.onnx) \| COCO \| Int8 \| 640x640x3 \| STM32N6570-DK \| NPU/MCU \| 3417.54 \| 0.29 \| 3.0.0 \|

	### 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 \|
	\|-------\|---------\|--------\|------------\|--------\|-------------------\|-------------------\|--------------------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192_qdq_int8.onnx) \| COCO-Person \| Int8 \| 192x192x3 \| STM32N6 \| 1008.00 \| 0 \| 3155.48 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256_qdq_int8.onnx) \| COCO-Person \| Int8 \| 256x256x3 \| STM32N6 \| 2320.00 \| 0 \| 3164.48 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320_qdq_int8.onnx) \| COCO-Person \| Int8 \| 320x320x3 \| STM32N6 \| 2743.88 \| 800.00 \| 3167.85 \| 3.0.0 \|

	### 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 \| STEdgeAI Core version \|
	\|-------\|---------\|--------\|------------\|-------\|------------------\|--------------------\|-----------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192_qdq_int8.onnx) \| COCO-Person \| Int8 \| 192x192x3 \| STM32N6570-DK \| NPU/MCU \| 17.90 \| 55.87 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256_qdq_int8.onnx) \| COCO-Person \| Int8 \| 256x256x3 \| STM32N6570-DK \| NPU/MCU \| 28.36 \| 35.26 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320_qdq_int8.onnx) \| COCO-Person \| Int8 \| 320x320x3 \| STM32N6570-DK \| NPU/MCU \| 68.54 \| 14.59 \| 3.0.0 \|



	### AP on COCO dataset

	Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode), Number of classes: 80

	\| Model \| Format \| Resolution \| AP50 \|
	\| --- \| --- \| --- \| --- \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192.onnx) \| Float \| 3x192x192 \| 35.42 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192_qdq_int8.onnx) \| Int8 \| 3x192x192 \| 32.29 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320.onnx) \| Float \| 3x320x320 \| 45.64 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320_qdq_int8.onnx) \| Int8 \| 3x320x320 \| 43.79 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640.onnx) \| Float \| 3x640x640 \| 52.64 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640_qdq_int8.onnx) \| Int8 \| 3x640x640 \| 51.17 \|

	\* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100

	### 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) , Number of classes: 1


	\| Model \| Format \| Resolution \| AP50 \|
	\| --- \| --- \| --- \| --- \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192.onnx) \| Float \| 3x192x192 \| 61.71 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192_qdq_int8.onnx) \| Int8 \| 3x192x192 \| 59.91 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256.onnx) \| Float \| 3x256x256 \| 68.07 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256_qdq_int8.onnx) \| Int8 \| 3x256x256 \| 66.16 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320.onnx) \| Float \| 3x320x320 \| 72.12 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320_qdq_int8.onnx) \| Int8 \| 3x320x320 \| 70.91 \|

	\* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100

	## Retraining and Integration in a simple example:

	Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)


	## References


	- STYOLO / STResNet paper
	[S. Sah & R. Kumar, STResNet & STYOLO: A New Family of Compact Classification and Object Detection Models for MCUs](https://arxiv.org/abs/2601.05364)

	- YOLOX (inspires STYOLO architecture)
	[Ge et al., YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430)

	- MS COCO dataset
	[Lin et al., Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)

	---
	license: other
	license_name: sla0044
	license_link: >-
	https://github.com/STMicroelectronics/stm32ai-modelzoo/blob/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/LICENSE.md
	pipeline_tag: object-detection
	---
	# STYOLOMilli

	## Use case : `Object detection`

	## Model description

	STYOLOMilli is a compact and efficient object detection model designed for deployment on resource‑constrained hardware such as microcontroller units (MCUs) and neural processing units (NPUs). It is part of the STYOLO family introduced alongside STResNet in the paper [STResNet & STYOLO: A New Family of Compact Classification and Object Detection Models for MCUs](https://arxiv.org/abs/2601.05364).

	The model builds on a highly compressed backbone derived from the STResNet design and integrates it within a YOLOX‑style architecture optimized for low memory usage, high efficiency, and competitive accuracy. STYOLOMilli delivers strong object detection performance (33.6 mAP on MS COCO) while maintaining a very small model footprint, outperforming or matching other micro sized detectors such as YOLOv5n and YOLOX‑Nano on the same tasks.

	The `st_yolo_milli_pt` variant is implemented in PyTorch and is tuned for ultra‑efficient inference on edge and MCU environments.

	## Network information

	\| Network information \| Value \|
	\|--------------------\|-------\|
	\| Framework \| Torch \|
	\| Quantization \| Int8 \|
	\| Provenance \| [STMicroelectronics Model Zoo Services](https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main/object_detection/object_detection) \|
	\| Paper \| [STResNet & STYOLO (arXiv:2601.05364)](https://arxiv.org/abs/2601.05364) \|

	The model is quantized to int8 using ONNX Runtime and exported for efficient deployment.


	## Network inputs / outputs

	For an image resolution of NxM and NC classes

	\| Input Shape \| Description \|
	\| ----- \| ----------- \|
	\| (1, W, H, 3) \| Single NxM RGB image with UINT8 values between 0 and 255 \|

	\| Output Shape \| Description \|
	\| ----- \| ----------- \|
	\| (1, (W/8xH/8 + W/16xH/16 + W/32xH/32), (NC+1+4)) \| Model returns bounding boxes with 6 values for each box, four coordinates (x1,y1,x2,y2), class confidence and objectness confidence \|


	## Recommended Platforms

	\| Platform \| Supported \| Recommended \|
	\|----------\|-----------\|-------------\|
	\| STM32L0 \| [] \| [] \|
	\| STM32L4 \| [] \| [] \|
	\| STM32U5 \| [] \| [] \|
	\| STM32H7 \| [] \| [] \|
	\| STM32MP1 \| [] \| [] \|
	\| STM32MP2 \| [] \| [] \|
	\| STM32N6 \| [x] \| [x] \|


	# Performances

	## Metrics

	Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option.

	### 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 \|
	\|-------\|---------\|--------\|------------\|--------\|-------------------\|-------------------\|--------------------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192_qdq_int8.onnx) \| COCO \| Int8 \| 192x192x3 \| STM32N6 \| 1008.00 \| 0 \| 3170.29 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320_qdq_int8.onnx) \| COCO \| Int8 \| 320x320x3 \| STM32N6 \| 2744.48 \| 800.00 \| 3182.67 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640_qdq_int8.onnx) \| COCO \| Int8 \| 640x640x3 \| STM32N6 \| 2768.00 \| 9600.00 \| 3189.38 \| 3.0.0 \|

	* 640x640 coco checkpoints are provided primarily for finetuning purposes as these checkpoints are trained on large dataset at higher resolution. Models with 640 resolution is not suitable for deployment.

	### 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 \|
	\|-------\|---------\|--------\|------------\|-------\|------------------\|--------------------\|-----------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192_qdq_int8.onnx) \| COCO \| Int8 \| 192x192x3 \| STM32N6570-DK \| NPU/MCU \| 18.78 \| 53.25 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320_qdq_int8.onnx) \| COCO \| Int8 \| 320x320x3 \| STM32N6570-DK \| NPU/MCU \| 70.57 \| 14.17 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640_qdq_int8.onnx) \| COCO \| Int8 \| 640x640x3 \| STM32N6570-DK \| NPU/MCU \| 3417.54 \| 0.29 \| 3.0.0 \|

	### 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 \|
	\|-------\|---------\|--------\|------------\|--------\|-------------------\|-------------------\|--------------------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192_qdq_int8.onnx) \| COCO-Person \| Int8 \| 192x192x3 \| STM32N6 \| 1008.00 \| 0 \| 3155.48 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256_qdq_int8.onnx) \| COCO-Person \| Int8 \| 256x256x3 \| STM32N6 \| 2320.00 \| 0 \| 3164.48 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320_qdq_int8.onnx) \| COCO-Person \| Int8 \| 320x320x3 \| STM32N6 \| 2743.88 \| 800.00 \| 3167.85 \| 3.0.0 \|

	### 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 \| STEdgeAI Core version \|
	\|-------\|---------\|--------\|------------\|-------\|------------------\|--------------------\|-----------\|-----------------------\|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192_qdq_int8.onnx) \| COCO-Person \| Int8 \| 192x192x3 \| STM32N6570-DK \| NPU/MCU \| 17.90 \| 55.87 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256_qdq_int8.onnx) \| COCO-Person \| Int8 \| 256x256x3 \| STM32N6570-DK \| NPU/MCU \| 28.36 \| 35.26 \| 3.0.0 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320_qdq_int8.onnx) \| COCO-Person \| Int8 \| 320x320x3 \| STM32N6570-DK \| NPU/MCU \| 68.54 \| 14.59 \| 3.0.0 \|



	### AP on COCO dataset

	Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode), Number of classes: 80

	\| Model \| Format \| Resolution \| AP50 \|
	\| --- \| --- \| --- \| --- \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192.onnx) \| Float \| 3x192x192 \| 35.42 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_192/st_yolodv2milli_actrelu_pt_coco_192_qdq_int8.onnx) \| Int8 \| 3x192x192 \| 32.29 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320.onnx) \| Float \| 3x320x320 \| 45.64 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_320/st_yolodv2milli_actrelu_pt_coco_320_qdq_int8.onnx) \| Int8 \| 3x320x320 \| 43.79 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640.onnx) \| Float \| 3x640x640 \| 52.64 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco/st_yolodv2milli_actrelu_pt_coco_640/st_yolodv2milli_actrelu_pt_coco_640_qdq_int8.onnx) \| Int8 \| 3x640x640 \| 51.17 \|

	\* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100

	### 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) , Number of classes: 1


	\| Model \| Format \| Resolution \| AP50 \|
	\| --- \| --- \| --- \| --- \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192.onnx) \| Float \| 3x192x192 \| 61.71 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_192/st_yolodv2milli_actrelu_pt_coco_person_192_qdq_int8.onnx) \| Int8 \| 3x192x192 \| 59.91 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256.onnx) \| Float \| 3x256x256 \| 68.07 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_256/st_yolodv2milli_actrelu_pt_coco_person_256_qdq_int8.onnx) \| Int8 \| 3x256x256 \| 66.16 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320.onnx) \| Float \| 3x320x320 \| 72.12 \|
	\| [st_yolodv2milli_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolodv2milli_pt/ST_pretrainedmodel_public_dataset/coco_person/st_yolodv2milli_actrelu_pt_coco_person_320/st_yolodv2milli_actrelu_pt_coco_person_320_qdq_int8.onnx) \| Int8 \| 3x320x320 \| 70.91 \|

	\* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100

	## Retraining and Integration in a simple example:

	Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)


	## References


	- STYOLO / STResNet paper
	[S. Sah & R. Kumar, STResNet & STYOLO: A New Family of Compact Classification and Object Detection Models for MCUs](https://arxiv.org/abs/2601.05364)

	- YOLOX (inspires STYOLO architecture)
	[Ge et al., YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430)

	- MS COCO dataset
	[Lin et al., Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)