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README.md
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YOLO algorithm is the most typical representative of one-stage target detection algorithm.
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It is based on deep neural network for object recognition and positioning. It runs very fast and can be used in real-time systems. YOLOv8 is currently the most advanced algorithm of the YOLO series, surpassing the previous YOLO series in terms of accuracy and speed.
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YOLO algorithm is the most typical representative of one-stage target detection algorithm.
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It is based on deep neural network for object recognition and positioning. It runs very fast and can be used in real-time systems. YOLOv8 is currently the most advanced algorithm of the YOLO series, surpassing the previous YOLO series in terms of accuracy and speed.
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The model can be found [here](https://github.com/ultralytics/ultralytics)
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|Device|SoC|Runtime|Model|Size (pixels)|Inference Time (ms)|Precision|Compute Unity|Model Download|
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|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
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|AidBox QCS6490|QCS6490|QNN|YOLOv8s(cutoff)|640|11.1|INT8|NPU|[model download]()|
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|AidBox QCS6490|QCS6490|QNN|YOLOv8s(cutoff)|640|24.8|INT16|NPU|[model download]()|
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|AidBox QCS6490|QCS6490|SNPE|YOLOv8s(cutoff)|640|9.6|INT8|NPU|[model download]()|
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|AidBox QCS6490|QCS6490|SNPE|YOLOv8s(cutoff)|640|22.1|INT16|NPU|[model download]()|
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|AidBox GS865|QCS8250|SNPE|YOLOv8s(cutoff)|640|35|INT8|NPU|[model download]()|
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|APLUX QCS8550|QCS8550|QNN|YOLOv8s(cutoff)|640|8.7|INT8|NPU|[model download](https://huggingface.co/aidlux/YOLOv8/blob/main/QCS8550/cutoff_yolov8s_int8.qnn.serialized.bin)|
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|APLUX QCS8550|QCS8550|QNN|YOLOv8s(cutoff)|640|20.3|INT16|NPU|[model download](https://huggingface.co/aidlux/YOLOv8/blob/main/QCS8550/cutoff_yolov8s_int16.qnn.serialized.bin)|
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|APLUX QCS8550|QCS8550|SNPE|YOLOv8s(cutoff)|640|3.8|INT8|NPU|[model download](https://huggingface.co/aidlux/YOLOv8/blob/main/QCS8550/cutoff_yolov8s_int8_htp_snpe2.dlc)|
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|APLUX QCS8550|QCS8550|SNPE|YOLOv8s(cutoff)|640|9.3|INT16|NPU|[model download](https://huggingface.co/aidlux/YOLOv8/blob/main/QCS8550/cutoff_yolov8s_int16_htp_snpe2.dlc)|
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