2021.2.6 此?目不再更新,新?目地址: Yolo-Fastest: Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest Yolo-Fastest: Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest 此表中NCNN基准未更新最新ARM82?据,最新版本NCNN理?上ARM82?有一倍速度提升,待更新... 添加基于ncnn的106??点 C sample: https://github.com/dog-qiuqiu/MobileNet-Yolo/tree/master/sample/ncnn ***Darknet Group convolution is not well supported on some GPUs such as NVIDIA PASCAL!!! AlexeyAB/darknet#6091 (comment) ??某些Pascal??例如1080ti在darknet上 ??失?/???常?慢/推理速度?常 的可以采用Pytorch版yolo3?架 ??/推理 https://github.com/dog-qiuqiu/yolov3 MobileNetV2-YOLOv3-Lite&Nano Darknet Mobile inference frameworks benchmark (4*ARM_CPU) Network VOC mAP(0.5) COCO mAP(0.5) Resolution Inference time (NCNN/Kirin 990) Inference time (MNN arm82/Kirin 990) FLOPS Weight size MobileNetV2-YOLOv3-Lite (our) 73.26 37.44 320 28.42 ms 18 ms 1.8BFlops 8.0MB MobileNetV2-YOLOv3-Nano (our) 65.27 30.13 320 10.16 ms 5 ms 0.5BFlops 3.0MB MobileNetV2-YOLOv3 70.7 & 352 32.15 ms & ms 2.44BFlops 14.4MB MobileNet-SSD 72.7 & 300 26.37 ms & ms & BFlops 23.1MB YOLOv5s & 56.2 416 150.5 ms & ms 13.2BFlops 28.1MB YOLOv3-Tiny-Prn & 33.1 416 36.6 ms & ms 3.5BFlops 18.8MB YOLOv4-Tiny & 40.2 416 44.6 ms & ms 6.9BFlops 23.1MB YOLO-Nano 69.1 & 416 & ms & ms 4.57BFlops 4.0MB Support mobile inference frameworks such as NCNN&MNN The mnn benchmark only includes the forward inference time The ncnn benchmark is the forward inference time + post-processing time(NMS...) of the convolution feature map. Darknet Train Configuration: CUDA-version: 10010 (10020), cuDNN: 7.6.4,OpenCV version: 4 GPU:RTX2080ti MobileNetV2-YOLOv3-Lite-COCO Test results Application Ultralight-SimplePose A ultra-lightweight human body posture key point prediction model designed for mobile devices, which can cooperate with MobileNetV2-YOLOv3-Nano to complete the human body posture estimation task https://github.com/dog-qiuqiu/Ultralight-SimplePose YoloFace-500k: 500kb yolo-Face-Detection Network Resolution Inference time (NCNN/Kirin 990) Inference time (MNN arm82/Kirin 990) FLOPS Weight size UltraFace-version-RFB 320x240 &ms 3.36ms 0.1BFlops 1.3MB UltraFace-version-Slim 320x240 &ms 3.06ms 0.1BFlops 1.2MB yoloface-500k 320x256 5.5ms 2.4ms 0.1BFlops 0.52MB yoloface-500k-v2 352x288 4.7ms &ms 0.1BFlops 0.42MB 都500k了,要?mAP:sunglasses: Inference time (DarkNet/i7-6700):13ms The mnn benchmark only includes the forward inference time The ncnn benchmark is the forward inference time + post-processing time(NMS...) of the convolution feature map. Wider Face Val Model Easy Set Medium Set Hard Set libfacedetection v1(caffe) 0.65 0.5 0.233 libfacedetection v2(caffe) 0.714 0.585 0.306 Retinaface-Mobilenet-0.25 (Mxnet) 0.745 0.553 0.232 version-slim-320 0.77 0.671 0.395 version-RFB-320 0.787 0.698 0.438 yoloface-500k-320 0.728 0.682 0.431 yoloface-500k-352-v2 0.768 0.729 0.490 yoloface-500k-v2:The SE&CSP module is added V2 does not support MNN temporarily wider_face_val(ap05): yoloface-500k: 53.75 yoloface-500k-v2: 56.69 YoloFace-500k Test results(thresh 0.7) YoloFace-500k-v2 Test results(thresh 0.7) YoloFace-50k: Sub-millisecond face detection model Network Resolution Inference time (NCNN/Kirin 990) Inference time (MNN arm82/Kirin 990) Inference time (DarkNet/R3-3100) FLOPS Weight size yoloface-50k 56x56 0.27ms 0.31ms 0.5 ms 0.001BFlops 46kb For the close-range face detection model in a specific scene, the recommended detection distance is 1.5m YoloFace-50k Test results(thresh 0.7) YoloFace50k-landmark106(Ultra lightweight 106 point face-landmark model) Network Resolution Inference time (NCNN/Kirin 990) Inference time (MNN arm82/Kirin 990) Weight size landmark106 112x112 0.6ms 0.5ms 1.4MB Face detection: yoloface-50k Landmark: landmark106 YoloFace50k-landmark106 Test results Reference&Framework instructions&How to Train https://github.com/AlexeyAB/darknet You must use a pre-trained model to train your own data set. You can make a pre-trained model based on the weights of COCO training in this project to initialize the network parameters 交流qq群:1062122604 About model selection MobileNetV2-YOLOv3-SPP: Nvidia Jeston, Intel Movidius, TensorRT,NPU,OPENVINO...High-performance embedded side MobileNetV2-YOLOv3-Lite: High Performance ARM-CPU,Qualcomm Adreno GPU, ARM82...High-performance mobile MobileNetV2-YOLOv3-NANO: ARM-CPU...Computing resources are limited MobileNetV2-YOLOv3-Fastest: ....... Can you do personal face detection???It’s better than nothing NCNN conversion tutorial Benchmark: https://github.com/Tencent/ncnn/tree/master/benchmark NCNN supports direct conversion of darknet models darknet2ncnn: https://github.com/Tencent/ncnn/tree/master/tools/darknet NCNN C++ Sample https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/sample/ncnn NCNN Android Sample https://github.com/dog-qiuqiu/Android_MobileNetV2-YOLOV3-Nano-NCNN APK: https://github.com/dog-qiuqiu/Android_MobileNetV2-YOLOV3-Nano-NCNN/blob/master/app/release/MobileNetv2-yolov3-nano.apk DarkNet2Caffe tutorial Environmental requirements Python2.7 python-opencv Caffe(add upsample layer https://github.com/dog-qiuqiu/caffe ) You have to compile cpu version of caffe!!! cd darknet2caffe/ python darknet2caffe.py MobileNetV2-YOLOv3-Nano-voc.cfg MobileNetV2-YOLOv3-Nano-voc.weights MobileNetV2-YOLOv3-Nano-voc.prototxt MobileNetV2-YOLOv3-Nano-voc.caffemodel cp MobileNetV2-YOLOv3-Nano-voc.prototxt sample cp MobileNetV2-YOLOv3-Nano-voc.caffemodel sample cd sample python detector.py MNN conversion tutorial Benchmark: https://www.yuque.com/mnn/cn/tool_benchmark Convert darknet model to caffemodel through darknet2caffe Manually replace the upsample layer in prototxt with the interp layer Take the modification of MobileNetV2-YOLOv3-Nano-voc.prototxt as an example #layer { # bottom: "layer71-route" # top: "layer72-upsample" # name: "layer72-upsample" # type: "Upsample" # upsample_param { # scale: 2 # } #} layer { bottom: "layer71-route" top: "layer72-upsample" name: "layer72-upsample" type: "Interp" interp_param { height:20 #upsample h size width:20 #upsample w size } } MNN conversion: https://www.yuque.com/mnn/cn/model_convert Thanks https://github.com/shicai/MobileNet-Caffe https://github.com/WZTENG/YOLOv5_NCNN https://github.com/AlexeyAB/darknet https://github.com/Tencent/ncnn https://gluon-cv.mxnet.io/