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Yolo-Fastest
âš¡ Based on yolo's ultra-lightweight universal target detection algorithm, the calculation amount is only 250mflops, the ncnn model size is only 666kb, the Raspberry Pi 3b can run up to 15fps+, and the mobile terminal can run up to 178fps+MobileNet-Yolo
MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB🔥🔥🔥Yolo-FastestV2
âš¡ Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+FastestDet
⚡ A newly designed ultra lightweight anchor free target detection algorithm, weight only 250K parameters, reduces the time consumption by 10% compared with yolo-fastest, and the post-processing is simplerUltralight-SimplePose
Ultra-lightweight human body posture key point CNN model. ModelSize:2.3MB HUAWEI P40 NCNN benchmark: 6ms/img,simple-rknn2
The rknn2 API uses the secondary encapsulation of the process, which is easy for everyone to call. It is applicable to rk356x rk3588Android_MobileNetV2-YOLOV3-Nano-NCNN
MobileNetV2-YOLOV3-Nano NCNN samplelibopencv
Opencv ARM Linux precompiled librarySimple-TensorRT
Secondary encapsulation of NVIDIA TensorRT interface to simplify the calling processdog-qiuqiu
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