Ecosyste.ms: Awesome

https://github.com/dog-qiuqiu/Yolo-Fastest

:zap: 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+

Last synced: 31 Jul 2024

https://github.com/dog-qiuqiu/MobileNet-Yolo

MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:

cnn computer-vision cv darknet deep-learning face-detection landmark landmark-detection mnn mnn-framework mobilenet-yolo mobilenetv2 ncnn ncnn-model object-detection yolo yolov3

Last synced: 31 Jul 2024

https://github.com/dog-qiuqiu/Yolo-FastestV2

:zap: 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+

Last synced: 31 Jul 2024

https://github.com/dog-qiuqiu/FastestDet

:zap: 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 simpler

computer-vision deep-learning object-detection

Last synced: 30 Jul 2024

https://github.com/dog-qiuqiu/simple-rknn2

The rknn2 API uses the secondary encapsulation of the process, which is easy for everyone to call. It is applicable to rk356x rk3588

Last synced: 31 Jul 2024